first generate

Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/Compiler/EventRecorderConf.h

@@ -0,0 +1,44 @@
+/*------------------------------------------------------------------------------
+ * MDK - Component ::Event Recorder
+ * Copyright (c) 2016 ARM Germany GmbH. All rights reserved.
+ *------------------------------------------------------------------------------
+ * Name:    EventRecorderConf.h
+ * Purpose: Event Recorder Configuration
+ * Rev.:    V1.0.0
+ *----------------------------------------------------------------------------*/
+
+//-------- <<< Use Configuration Wizard in Context Menu >>> --------------------
+
+// <h>Event Recorder
+
+//   <o>Number of Records
+//     <8=>8 <16=>16 <32=>32 <64=>64 <128=>128 <256=>256 <512=>512 <1024=>1024
+//     <2048=>2048 <4096=>4096 <8192=>8192 <16384=>16384 <32768=>32768
+//     <65536=>65536 <131072=>131072 <262144=>262144 <524288=>524288
+//     <1048576=>1048576
+//   <i>Configure size of Event Record Buffer (each record is 16 bytes)
+//   <i>Must be 2^n (min=8, max=1048576)
+#define EVENT_RECORD_COUNT      64U
+
+//   <o>Time Stamp Source
+//      <0=> DWT Cycle Counter  <1=> SysTick
+//      <3=> User Timer (Normal Reset)  <4=> User Timer (Power-On Reset)
+//   <i>Selects source for 32-bit time stamp
+#define EVENT_TIMESTAMP_SOURCE  1
+
+//   <h>SysTick Configuration
+//   <i>Configure values when Time Stamp Source is set to SysTick
+
+//     <o>SysTick Input Clock Frequency [Hz] <1-1000000000>
+//     <i>Defines SysTick input clock (typical identical with processor clock)
+#define SYSTICK_CLOCK           100000000U
+
+//     <o>SysTick Interrupt Period [us] <1-1000000000>
+//     <i>Defines time period of the SysTick timer interrupt
+#define SYSTICK_PERIOD_US       1000U
+
+//   </h>
+
+// </h>
+
+//------------- <<< end of configuration section >>> ---------------------------

Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/_ARMCM0/RTE_Components.h

@@ -0,0 +1,24 @@
+
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ *      *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_cifar10' 
+ * Target:  'ARMCM0' 
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File: 
+ */
+#define CMSIS_device_header "ARMCM0.h"
+
+#define RTE_Compiler_EventRecorder
+          #define RTE_Compiler_EventRecorder_DAP
+#define RTE_Compiler_IO_STDOUT          /* Compiler I/O: STDOUT */
+          #define RTE_Compiler_IO_STDOUT_EVR      /* Compiler I/O: STDOUT EVR */
+
+#endif /* RTE_COMPONENTS_H */

Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/_ARMCM3/RTE_Components.h

@@ -0,0 +1,22 @@
+
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ *      *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_cifar10' 
+ * Target:  'ARMCM3' 
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File: 
+ */
+#define CMSIS_device_header "ARMCM3.h"
+
+#define RTE_Compiler_IO_STDOUT          /* Compiler I/O: STDOUT */
+          #define RTE_Compiler_IO_STDOUT_ITM      /* Compiler I/O: STDOUT ITM */
+
+#endif /* RTE_COMPONENTS_H */

Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/_ARMCM4_FP/RTE_Components.h

@@ -0,0 +1,22 @@
+
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ *      *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_cifar10' 
+ * Target:  'ARMCM4_FP' 
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File: 
+ */
+#define CMSIS_device_header "ARMCM4_FP.h"
+
+#define RTE_Compiler_IO_STDOUT          /* Compiler I/O: STDOUT */
+          #define RTE_Compiler_IO_STDOUT_ITM      /* Compiler I/O: STDOUT ITM */
+
+#endif /* RTE_COMPONENTS_H */

Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/RTE/_ARMCM7_SP/RTE_Components.h

@@ -0,0 +1,22 @@
+
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ *      *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_cifar10' 
+ * Target:  'ARMCM7_SP' 
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File: 
+ */
+#define CMSIS_device_header "ARMCM7_SP.h"
+
+#define RTE_Compiler_IO_STDOUT          /* Compiler I/O: STDOUT */
+          #define RTE_Compiler_IO_STDOUT_ITM      /* Compiler I/O: STDOUT ITM */
+
+#endif /* RTE_COMPONENTS_H */

Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10.cpp

@@ -0,0 +1,196 @@
+/* ----------------------------------------------------------------------
+* Copyright (C) 2010-2018 Arm Limited. All rights reserved.
+*
+*
+* Project:       CMSIS NN Library
+* Title:         arm_nnexamples_cifar10.cpp
+*
+* Description:   Convolutional Neural Network Example
+*
+* Target Processor: Cortex-M4/Cortex-M7
+*
+* Redistribution and use in source and binary forms, with or without
+* modification, are permitted provided that the following conditions
+* are met:
+*   - Redistributions of source code must retain the above copyright
+*     notice, this list of conditions and the following disclaimer.
+*   - Redistributions in binary form must reproduce the above copyright
+*     notice, this list of conditions and the following disclaimer in
+*     the documentation and/or other materials provided with the
+*     distribution.
+*   - Neither the name of Arm LIMITED nor the names of its contributors
+*     may be used to endorse or promote products derived from this
+*     software without specific prior written permission.
+*
+* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+* POSSIBILITY OF SUCH DAMAGE.
+* -------------------------------------------------------------------- */
+
+/**
+ * @ingroup groupExamples
+ */
+
+/**
+ * @defgroup CNNExample Convolutional Neural Network Example
+ *
+ * \par Description:
+ * \par
+ * Demonstrates a convolutional neural network (CNN) example with the use of convolution,
+ * ReLU activation, pooling and fully-connected functions.
+ *
+ * \par Model definition:
+ * \par
+ * The CNN used in this example is based on CIFAR-10 example from Caffe [1]. 
+ * The neural network consists
+ * of 3 convolution layers interspersed by ReLU activation and max pooling layers, followed by a 
+ * fully-connected layer at the end. The input to the network is a 32x32 pixel color image, which will 
+ * be classified into one of the 10 output classes. 
+ * This example model implementation needs 32.3 KB to store weights, 40 KB for activations and 
+ * 3.1 KB for storing the \c im2col data.
+ *
+ * \image html CIFAR10_CNN.gif "Neural Network model definition"
+ *
+ * \par Variables Description:
+ * \par
+ * \li \c conv1_wt, \c conv2_wt, \c conv3_wt are convolution layer weight matrices
+ * \li \c conv1_bias, \c conv2_bias, \c conv3_bias are convolution layer bias arrays
+ * \li \c ip1_wt, ip1_bias point to fully-connected layer weights and biases
+ * \li \c input_data points to the input image data
+ * \li \c output_data points to the classification output
+ * \li \c col_buffer is a buffer to store the \c im2col output
+ * \li \c scratch_buffer is used to store the activation data (intermediate layer outputs)
+ *
+ * \par CMSIS DSP Software Library Functions Used:
+ * \par
+ * - arm_convolve_HWC_q7_RGB()
+ * - arm_convolve_HWC_q7_fast()
+ * - arm_relu_q7()
+ * - arm_maxpool_q7_HWC()
+ * - arm_avepool_q7_HWC()
+ * - arm_fully_connected_q7_opt()
+ * - arm_fully_connected_q7()
+ *
+ * <b> Refer  </b>
+ * \link arm_nnexamples_cifar10.cpp \endlink
+ *
+ * \par [1] https://github.com/BVLC/caffe
+ */
+
+#include <stdint.h>
+#include <stdio.h>
+#include "arm_math.h"
+#include "arm_nnexamples_cifar10_parameter.h"
+#include "arm_nnexamples_cifar10_weights.h"
+
+#include "arm_nnfunctions.h"
+#include "arm_nnexamples_cifar10_inputs.h"
+
+#ifdef _RTE_
+#include "RTE_Components.h"
+#ifdef RTE_Compiler_EventRecorder
+#include "EventRecorder.h"
+#endif
+#endif
+
+// include the input and weights
+
+static q7_t conv1_wt[CONV1_IM_CH * CONV1_KER_DIM * CONV1_KER_DIM * CONV1_OUT_CH] = CONV1_WT;
+static q7_t conv1_bias[CONV1_OUT_CH] = CONV1_BIAS;
+
+static q7_t conv2_wt[CONV2_IM_CH * CONV2_KER_DIM * CONV2_KER_DIM * CONV2_OUT_CH] = CONV2_WT;
+static q7_t conv2_bias[CONV2_OUT_CH] = CONV2_BIAS;
+
+static q7_t conv3_wt[CONV3_IM_CH * CONV3_KER_DIM * CONV3_KER_DIM * CONV3_OUT_CH] = CONV3_WT;
+static q7_t conv3_bias[CONV3_OUT_CH] = CONV3_BIAS;
+
+static q7_t ip1_wt[IP1_DIM * IP1_OUT] = IP1_WT;
+static q7_t ip1_bias[IP1_OUT] = IP1_BIAS;
+
+/* Here the image_data should be the raw uint8 type RGB image in [RGB, RGB, RGB ... RGB] format */
+uint8_t   image_data[CONV1_IM_CH * CONV1_IM_DIM * CONV1_IM_DIM] = IMG_DATA;
+q7_t      output_data[IP1_OUT];
+
+//vector buffer: max(im2col buffer,average pool buffer, fully connected buffer)
+q7_t      col_buffer[2 * 5 * 5 * 32 * 2];
+
+q7_t      scratch_buffer[32 * 32 * 10 * 4];
+
+int main()
+{
+  #ifdef RTE_Compiler_EventRecorder
+  EventRecorderInitialize (EventRecordAll, 1);  // initialize and start Event Recorder
+  #endif
+
+  printf("start execution\n");
+  /* start the execution */
+
+  q7_t     *img_buffer1 = scratch_buffer;
+  q7_t     *img_buffer2 = img_buffer1 + 32 * 32 * 32;
+
+  /* input pre-processing */
+  int mean_data[3] = INPUT_MEAN_SHIFT;
+  unsigned int scale_data[3] = INPUT_RIGHT_SHIFT;
+  for (int i=0;i<32*32*3; i+=3) {
+    img_buffer2[i] =   (q7_t)__SSAT( ((((int)image_data[i]   - mean_data[0])<<7) + (0x1<<(scale_data[0]-1)))
+                             >> scale_data[0], 8);
+    img_buffer2[i+1] = (q7_t)__SSAT( ((((int)image_data[i+1] - mean_data[1])<<7) + (0x1<<(scale_data[1]-1)))
+                             >> scale_data[1], 8);
+    img_buffer2[i+2] = (q7_t)__SSAT( ((((int)image_data[i+2] - mean_data[2])<<7) + (0x1<<(scale_data[2]-1)))
+                             >> scale_data[2], 8);
+  }
+  
+  // conv1 img_buffer2 -> img_buffer1
+  arm_convolve_HWC_q7_RGB(img_buffer2, CONV1_IM_DIM, CONV1_IM_CH, conv1_wt, CONV1_OUT_CH, CONV1_KER_DIM, CONV1_PADDING,
+                          CONV1_STRIDE, conv1_bias, CONV1_BIAS_LSHIFT, CONV1_OUT_RSHIFT, img_buffer1, CONV1_OUT_DIM,
+                          (q15_t *) col_buffer, NULL);
+
+  arm_relu_q7(img_buffer1, CONV1_OUT_DIM * CONV1_OUT_DIM * CONV1_OUT_CH);
+
+  // pool1 img_buffer1 -> img_buffer2
+  arm_maxpool_q7_HWC(img_buffer1, CONV1_OUT_DIM, CONV1_OUT_CH, POOL1_KER_DIM,
+                     POOL1_PADDING, POOL1_STRIDE, POOL1_OUT_DIM, NULL, img_buffer2);
+
+  // conv2 img_buffer2 -> img_buffer1
+  arm_convolve_HWC_q7_fast(img_buffer2, CONV2_IM_DIM, CONV2_IM_CH, conv2_wt, CONV2_OUT_CH, CONV2_KER_DIM,
+                           CONV2_PADDING, CONV2_STRIDE, conv2_bias, CONV2_BIAS_LSHIFT, CONV2_OUT_RSHIFT, img_buffer1,
+                           CONV2_OUT_DIM, (q15_t *) col_buffer, NULL);
+
+  arm_relu_q7(img_buffer1, CONV2_OUT_DIM * CONV2_OUT_DIM * CONV2_OUT_CH);
+
+  // pool2 img_buffer1 -> img_buffer2
+  arm_maxpool_q7_HWC(img_buffer1, CONV2_OUT_DIM, CONV2_OUT_CH, POOL2_KER_DIM,
+                     POOL2_PADDING, POOL2_STRIDE, POOL2_OUT_DIM, col_buffer, img_buffer2);
+
+// conv3 img_buffer2 -> img_buffer1
+  arm_convolve_HWC_q7_fast(img_buffer2, CONV3_IM_DIM, CONV3_IM_CH, conv3_wt, CONV3_OUT_CH, CONV3_KER_DIM,
+                           CONV3_PADDING, CONV3_STRIDE, conv3_bias, CONV3_BIAS_LSHIFT, CONV3_OUT_RSHIFT, img_buffer1,
+                           CONV3_OUT_DIM, (q15_t *) col_buffer, NULL);
+
+  arm_relu_q7(img_buffer1, CONV3_OUT_DIM * CONV3_OUT_DIM * CONV3_OUT_CH);
+
+  // pool3 img_buffer-> img_buffer2
+  arm_maxpool_q7_HWC(img_buffer1, CONV3_OUT_DIM, CONV3_OUT_CH, POOL3_KER_DIM,
+                     POOL3_PADDING, POOL3_STRIDE, POOL3_OUT_DIM, col_buffer, img_buffer2);
+
+  arm_fully_connected_q7_opt(img_buffer2, ip1_wt, IP1_DIM, IP1_OUT, IP1_BIAS_LSHIFT, IP1_OUT_RSHIFT, ip1_bias,
+                             output_data, (q15_t *) img_buffer1);
+
+  arm_softmax_q7(output_data, 10, output_data);
+
+  for (int i = 0; i < 10; i++)
+  {
+      printf("%d: %d\n", i, output_data[i]);
+  }
+
+  return 0;
+}

Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/arm_nnexamples_cifar10_parameter.h

@@ -0,0 +1,43 @@
+#define CONV1_IM_DIM 32
+#define CONV1_IM_CH 3
+#define CONV1_KER_DIM 5
+#define CONV1_PADDING 2
+#define CONV1_STRIDE 1
+#define CONV1_OUT_CH 32
+#define CONV1_OUT_DIM 32
+
+#define POOL1_KER_DIM 3
+#define POOL1_STRIDE 2
+#define POOL1_PADDING 0
+#define POOL1_OUT_DIM 16
+
+#define CONV2_IM_DIM 16
+#define CONV2_IM_CH 32
+#define CONV2_KER_DIM 5
+#define CONV2_PADDING 2
+#define CONV2_STRIDE 1
+#define CONV2_OUT_CH 16
+#define CONV2_OUT_DIM 16
+
+#define POOL2_KER_DIM 3
+#define POOL2_STRIDE 2
+#define POOL2_PADDING 0
+#define POOL2_OUT_DIM 8
+
+#define CONV3_IM_DIM 8
+#define CONV3_IM_CH 16
+#define CONV3_KER_DIM 5
+#define CONV3_PADDING 2
+#define CONV3_STRIDE 1
+#define CONV3_OUT_CH 32
+#define CONV3_OUT_DIM 8
+
+#define POOL3_KER_DIM 3
+#define POOL3_STRIDE 2
+#define POOL3_PADDING 0
+#define POOL3_OUT_DIM 4
+
+#define IP1_DIM 4*4*32
+#define IP1_IM_DIM 4
+#define IP1_IM_CH 32
+#define IP1_OUT 10

Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/cifar10/readme.txt

@@ -0,0 +1,4 @@
+CMSIS NN Lib example arm_nnexample_cifar10 for
+  Cortex-M4 and Cortex-M7.
+
+The example is configured for uVision Simulator.

Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/Compiler/EventRecorderConf.h

@@ -0,0 +1,44 @@
+/*------------------------------------------------------------------------------
+ * MDK - Component ::Event Recorder
+ * Copyright (c) 2016 ARM Germany GmbH. All rights reserved.
+ *------------------------------------------------------------------------------
+ * Name:    EventRecorderConf.h
+ * Purpose: Event Recorder Configuration
+ * Rev.:    V1.0.0
+ *----------------------------------------------------------------------------*/
+
+//-------- <<< Use Configuration Wizard in Context Menu >>> --------------------
+
+// <h>Event Recorder
+
+//   <o>Number of Records
+//     <8=>8 <16=>16 <32=>32 <64=>64 <128=>128 <256=>256 <512=>512 <1024=>1024
+//     <2048=>2048 <4096=>4096 <8192=>8192 <16384=>16384 <32768=>32768
+//     <65536=>65536 <131072=>131072 <262144=>262144 <524288=>524288
+//     <1048576=>1048576
+//   <i>Configure size of Event Record Buffer (each record is 16 bytes)
+//   <i>Must be 2^n (min=8, max=1048576)
+#define EVENT_RECORD_COUNT      64U
+
+//   <o>Time Stamp Source
+//      <0=> DWT Cycle Counter  <1=> SysTick
+//      <3=> User Timer (Normal Reset)  <4=> User Timer (Power-On Reset)
+//   <i>Selects source for 32-bit time stamp
+#define EVENT_TIMESTAMP_SOURCE  1
+
+//   <h>SysTick Configuration
+//   <i>Configure values when Time Stamp Source is set to SysTick
+
+//     <o>SysTick Input Clock Frequency [Hz] <1-1000000000>
+//     <i>Defines SysTick input clock (typical identical with processor clock)
+#define SYSTICK_CLOCK           100000000U
+
+//     <o>SysTick Interrupt Period [us] <1-1000000000>
+//     <i>Defines time period of the SysTick timer interrupt
+#define SYSTICK_PERIOD_US       1000U
+
+//   </h>
+
+// </h>
+
+//------------- <<< end of configuration section >>> ---------------------------

Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/_ARMCM0/RTE_Components.h

@@ -0,0 +1,24 @@
+
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ *      *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_gru' 
+ * Target:  'ARMCM0' 
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File: 
+ */
+#define CMSIS_device_header "ARMCM0.h"
+
+#define RTE_Compiler_EventRecorder
+          #define RTE_Compiler_EventRecorder_DAP
+#define RTE_Compiler_IO_STDOUT          /* Compiler I/O: STDOUT */
+          #define RTE_Compiler_IO_STDOUT_EVR      /* Compiler I/O: STDOUT EVR */
+
+#endif /* RTE_COMPONENTS_H */

Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/_ARMCM3/RTE_Components.h

@@ -0,0 +1,22 @@
+
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ *      *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_gru' 
+ * Target:  'ARMCM3' 
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File: 
+ */
+#define CMSIS_device_header "ARMCM3.h"
+
+#define RTE_Compiler_IO_STDOUT          /* Compiler I/O: STDOUT */
+          #define RTE_Compiler_IO_STDOUT_ITM      /* Compiler I/O: STDOUT ITM */
+
+#endif /* RTE_COMPONENTS_H */

Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/_ARMCM4_FP/RTE_Components.h

@@ -0,0 +1,22 @@
+
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ *      *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_gru' 
+ * Target:  'ARMCM4_FP' 
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File: 
+ */
+#define CMSIS_device_header "ARMCM4_FP.h"
+
+#define RTE_Compiler_IO_STDOUT          /* Compiler I/O: STDOUT */
+          #define RTE_Compiler_IO_STDOUT_ITM      /* Compiler I/O: STDOUT ITM */
+
+#endif /* RTE_COMPONENTS_H */

Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/RTE/_ARMCM7_SP/RTE_Components.h

@@ -0,0 +1,22 @@
+
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ *      *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_gru' 
+ * Target:  'ARMCM7_SP' 
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File: 
+ */
+#define CMSIS_device_header "ARMCM7_SP.h"
+
+#define RTE_Compiler_IO_STDOUT          /* Compiler I/O: STDOUT */
+          #define RTE_Compiler_IO_STDOUT_ITM      /* Compiler I/O: STDOUT ITM */
+
+#endif /* RTE_COMPONENTS_H */

Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/arm_nnexamples_gru.cpp

@@ -0,0 +1,221 @@
+/* ----------------------------------------------------------------------
+* Copyright (C) 2010-2018 Arm Limited. All rights reserved.
+*
+*
+* Project:       CMSIS NN Library
+* Title:         arm_nnexamples_gru.cpp
+*
+* Description:   Gated Recurrent Unit Example
+*
+* Target Processor: Cortex-M4/Cortex-M7
+*
+* Redistribution and use in source and binary forms, with or without
+* modification, are permitted provided that the following conditions
+* are met:
+*   - Redistributions of source code must retain the above copyright
+*     notice, this list of conditions and the following disclaimer.
+*   - Redistributions in binary form must reproduce the above copyright
+*     notice, this list of conditions and the following disclaimer in
+*     the documentation and/or other materials provided with the
+*     distribution.
+*   - Neither the name of Arm LIMITED nor the names of its contributors
+*     may be used to endorse or promote products derived from this
+*     software without specific prior written permission.
+*
+* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+* POSSIBILITY OF SUCH DAMAGE.
+* -------------------------------------------------------------------- */
+
+/**
+ * @ingroup groupExamples
+ */
+
+/**
+ * @defgroup GRUExample Gated Recurrent Unit Example
+ *
+ * \par Description:
+ * \par
+ * Demonstrates a gated recurrent unit (GRU) example with the use of fully-connected,
+ * Tanh/Sigmoid activation functions.
+ *
+ * \par Model definition:
+ * \par
+ * GRU is a type of recurrent neural network (RNN). It contains two sigmoid gates and one hidden
+ * state. 
+ * \par
+ * The computation can be summarized as:
+ * <pre>z[t] = sigmoid( W_z &sdot; {h[t-1],x[t]} )
+ * r[t] = sigmoid( W_r &sdot; {h[t-1],x[t]} ) 
+ * n[t] = tanh( W_n &sdot; [r[t] &times; {h[t-1], x[t]} ) 
+ * h[t] = (1 - z[t]) &times; h[t-1] + z[t] &times; n[t] </pre>
+ * \image html GRU.gif "Gate Recurrent Unit Diagram"
+ *
+ * \par Variables Description:
+ * \par
+ * \li \c update_gate_weights, \c reset_gate_weights, \c hidden_state_weights are weights corresponding to update gate (W_z), reset gate (W_r), and hidden state (W_n).
+ * \li \c update_gate_bias, \c reset_gate_bias, \c hidden_state_bias are layer bias arrays
+ * \li \c test_input1, \c test_input2, \c test_history are the inputs and initial history
+ *
+ * \par
+ * The buffer is allocated as:
+ * \par
+ * | reset | input | history | update | hidden_state |
+ * \par
+ * In this way, the concatination is automatically done since (reset, input) and (input, history)
+ * are physically concatinated in memory.
+ * \par
+ *  The ordering of the weight matrix should be adjusted accordingly.
+ *
+  *
+ * 
+ * \par CMSIS DSP Software Library Functions Used:
+ * \par
+ * - arm_fully_connected_mat_q7_vec_q15_opt()
+ * - arm_nn_activations_direct_q15()
+ * - arm_mult_q15()
+ * - arm_offset_q15()
+ * - arm_sub_q15()
+ * - arm_copy_q15()
+ *
+ * <b> Refer  </b>
+ * \link arm_nnexamples_gru.cpp \endlink
+ *
+ */
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include "arm_nnexamples_gru_test_data.h"
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+#ifdef _RTE_
+#include "RTE_Components.h"
+#ifdef RTE_Compiler_EventRecorder
+#include "EventRecorder.h"
+#endif
+#endif
+
+#define DIM_HISTORY 32
+#define DIM_INPUT 32
+#define DIM_VEC 64
+
+#define USE_X4
+
+#ifndef USE_X4
+static q7_t update_gate_weights[DIM_VEC * DIM_HISTORY] = UPDATE_GATE_WEIGHT_X2;
+static q7_t reset_gate_weights[DIM_VEC * DIM_HISTORY] = RESET_GATE_WEIGHT_X2;
+static q7_t hidden_state_weights[DIM_VEC * DIM_HISTORY] = HIDDEN_STATE_WEIGHT_X2;
+#else
+static q7_t update_gate_weights[DIM_VEC * DIM_HISTORY] = UPDATE_GATE_WEIGHT_X4;
+static q7_t reset_gate_weights[DIM_VEC * DIM_HISTORY] = RESET_GATE_WEIGHT_X4;
+static q7_t hidden_state_weights[DIM_VEC * DIM_HISTORY] = HIDDEN_STATE_WEIGHT_X4;
+#endif
+
+static q7_t update_gate_bias[DIM_HISTORY] = UPDATE_GATE_BIAS;
+static q7_t reset_gate_bias[DIM_HISTORY] = RESET_GATE_BIAS;
+static q7_t hidden_state_bias[DIM_HISTORY] = HIDDEN_STATE_BIAS;
+
+static q15_t test_input1[DIM_INPUT] = INPUT_DATA1;
+static q15_t test_input2[DIM_INPUT] = INPUT_DATA2;
+static q15_t test_history[DIM_HISTORY] = HISTORY_DATA;
+
+q15_t     scratch_buffer[DIM_HISTORY * 4 + DIM_INPUT];
+
+void gru_example(q15_t * scratch_input, uint16_t input_size, uint16_t history_size,
+                 q7_t * weights_update, q7_t * weights_reset, q7_t * weights_hidden_state,
+                 q7_t * bias_update, q7_t * bias_reset, q7_t * bias_hidden_state)
+{
+  q15_t    *reset = scratch_input;
+  q15_t    *input = scratch_input + history_size;
+  q15_t    *history = scratch_input + history_size + input_size;
+  q15_t    *update = scratch_input + 2 * history_size + input_size;
+  q15_t    *hidden_state = scratch_input + 3 * history_size + input_size;
+
+  // reset gate calculation
+  // the range of the output can be adjusted with bias_shift and output_shift
+#ifndef USE_X4
+  arm_fully_connected_mat_q7_vec_q15(input, weights_reset, input_size + history_size, history_size, 0, 15, bias_reset,
+                                     reset, NULL);
+#else
+  arm_fully_connected_mat_q7_vec_q15_opt(input, weights_reset, input_size + history_size, history_size, 0, 15,
+                                         bias_reset, reset, NULL);
+#endif
+  // sigmoid function, the size of the integer bit-width should be consistent with out_shift
+  arm_nn_activations_direct_q15(reset, history_size, 0, ARM_SIGMOID);
+  arm_mult_q15(history, reset, reset, history_size);
+
+  // update gate calculation
+  // the range of the output can be adjusted with bias_shift and output_shift
+#ifndef USE_X4
+  arm_fully_connected_mat_q7_vec_q15(input, weights_update, input_size + history_size, history_size, 0, 15,
+                                     bias_update, update, NULL);
+#else
+  arm_fully_connected_mat_q7_vec_q15_opt(input, weights_update, input_size + history_size, history_size, 0, 15,
+                                         bias_update, update, NULL);
+#endif
+
+  // sigmoid function, the size of the integer bit-width should be consistent with out_shift
+  arm_nn_activations_direct_q15(update, history_size, 0, ARM_SIGMOID);
+
+  // hidden state calculation
+#ifndef USE_X4
+  arm_fully_connected_mat_q7_vec_q15(reset, weights_hidden_state, input_size + history_size, history_size, 0, 15,
+                                     bias_hidden_state, hidden_state, NULL);
+#else
+  arm_fully_connected_mat_q7_vec_q15_opt(reset, weights_hidden_state, input_size + history_size, history_size, 0, 15,
+                                         bias_hidden_state, hidden_state, NULL);
+#endif
+
+  // tanh function, the size of the integer bit-width should be consistent with out_shift
+  arm_nn_activations_direct_q15(hidden_state, history_size, 0, ARM_TANH);
+  arm_mult_q15(update, hidden_state, hidden_state, history_size);
+
+  // we calculate z - 1 here
+  // so final addition becomes substraction
+  arm_offset_q15(update, 0x8000, update, history_size);
+  // multiply history
+  arm_mult_q15(history, update, update, history_size);
+  // calculate history_out
+  arm_sub_q15(hidden_state, update, history, history_size);
+
+  return;
+}
+
+int main()
+{
+  #ifdef RTE_Compiler_EventRecorder
+  EventRecorderInitialize (EventRecordAll, 1);  // initialize and start Event Recorder
+  #endif
+
+  printf("Start GRU execution\n");
+  int       input_size = DIM_INPUT;
+  int       history_size = DIM_HISTORY;
+
+  // copy over the input data 
+  arm_copy_q15(test_input1, scratch_buffer + history_size, input_size);
+  arm_copy_q15(test_history, scratch_buffer + history_size + input_size, history_size);
+
+  gru_example(scratch_buffer, input_size, history_size,
+              update_gate_weights, reset_gate_weights, hidden_state_weights,
+              update_gate_bias, reset_gate_bias, hidden_state_bias);
+  printf("Complete first iteration on GRU\n");
+
+  arm_copy_q15(test_input2, scratch_buffer + history_size, input_size);
+  gru_example(scratch_buffer, input_size, history_size,
+              update_gate_weights, reset_gate_weights, hidden_state_weights,
+              update_gate_bias, reset_gate_bias, hidden_state_bias);
+  printf("Complete second iteration on GRU\n");
+
+  return 0;
+}

Drivers/CMSIS/NN/Examples/ARM/arm_nn_examples/gru/readme.txt

@@ -0,0 +1,4 @@
+CMSIS NN Lib example arm_nnexample_gru0 for
+  Cortex-M4 and Cortex-M7.
+
+The example is configured for uVision Simulator.

Drivers/CMSIS/NN/Include/arm_nn_tables.h

@@ -0,0 +1,59 @@
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nn_tables.h
+ * Description:  Extern declaration for NN tables
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ * -------------------------------------------------------------------- */
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef _ARM_NN_TABLES_H
+#define _ARM_NN_TABLES_H
+
+#include "arm_math.h"
+
+/**
+* @brief tables for various activation functions
+*
+*/
+
+extern const q15_t sigmoidTable_q15[256];
+extern const q7_t sigmoidTable_q7[256];
+
+extern const q7_t tanhTable_q7[256];
+extern const q15_t tanhTable_q15[256];
+
+  /**
+   * @brief 2-way tables for various activation functions
+   *
+   * 2-way table, H table for value larger than 1/4
+   * L table for value smaller than 1/4, H table for remaining
+   * We have this only for the q15_t version. It does not make
+   * sense to have it for q7_t type
+   */
+extern const q15_t sigmoidHTable_q15[192];
+extern const q15_t sigmoidLTable_q15[128];
+
+extern const q15_t sigmoidLTable_q15[128];
+extern const q15_t sigmoidHTable_q15[192];
+
+#endif                          /*  ARM_NN_TABLES_H */

Drivers/CMSIS/NN/Include/arm_nnsupportfunctions.h

@@ -0,0 +1,202 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nnsupportfunctions.h
+ * Description:  Public header file of support functions for CMSIS NN Library
+ *
+ * $Date:        13. July 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ * -------------------------------------------------------------------- */
+
+#ifndef _ARM_NNSUPPORTFUNCTIONS_H_
+#define _ARM_NNSUPPORTFUNCTIONS_H_
+
+#include "arm_math.h"
+#include "arm_common_tables.h"
+//#include <cstring>
+
+#ifdef __cplusplus
+extern    "C"
+{
+#endif
+
+/**
+ * @brief Union for SIMD access of Q31/Q15/Q7 types
+ */
+union arm_nnword
+{
+    q31_t     word;
+               /**< Q31 type */
+    q15_t     half_words[2];
+               /**< Q15 type */
+    q7_t      bytes[4];
+               /**< Q7 type */
+};
+
+/**
+ * @brief Struct for specifying activation function types
+ *
+ */
+typedef enum
+{
+    ARM_SIGMOID = 0,
+                /**< Sigmoid activation function */
+    ARM_TANH = 1,
+             /**< Tanh activation function */
+} arm_nn_activation_type;
+
+/**
+ * @defgroup nndata_convert Neural Network Data Conversion Functions
+ *
+ * Perform data type conversion in-between neural network operations
+ *
+ */
+
+/**
+ * @brief Converts the elements of the Q7 vector to Q15 vector without left-shift 
+ * @param[in]       *pSrc points to the Q7 input vector    
+ * @param[out]      *pDst points to the Q15 output vector   
+ * @param[in]       blockSize length of the input vector    
+ * @return none.    
+ *
+ */
+
+void      arm_q7_to_q15_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize);
+
+/**
+ * @brief  Converts the elements of the Q7 vector to reordered Q15 vector without left-shift
+ * @param[in]       *pSrc points to the Q7 input vector    
+ * @param[out]      *pDst points to the Q15 output vector   
+ * @param[in]       blockSize length of the input vector    
+ * @return none.    
+ *
+ */
+
+void      arm_q7_to_q15_reordered_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize);
+
+#if defined (ARM_MATH_DSP)
+
+/**
+ * @brief read and expand one Q7 word into two Q15 words
+ */
+
+__STATIC_FORCEINLINE void *read_and_pad(void *source, q31_t * out1, q31_t * out2)
+{
+        q31_t     inA = *__SIMD32(source)++;
+        q31_t     inAbuf1 = __SXTB16(__ROR(inA, 8));
+        q31_t     inAbuf2 = __SXTB16(inA);
+
+#ifndef ARM_MATH_BIG_ENDIAN
+        *out2 = __PKHTB(inAbuf1, inAbuf2, 16);
+        *out1 = __PKHBT(inAbuf2, inAbuf1, 16);
+#else
+        *out1 = __PKHTB(inAbuf1, inAbuf2, 16);
+        *out2 = __PKHBT(inAbuf2, inAbuf1, 16);
+#endif
+
+        return source;
+}
+
+/**
+ * @brief read and expand one Q7 word into two Q15 words with reordering
+ */
+
+__STATIC_FORCEINLINE void *read_and_pad_reordered(void *source, q31_t * out1, q31_t * out2)
+{
+        q31_t     inA = *__SIMD32(source)++;
+#ifndef ARM_MATH_BIG_ENDIAN
+        *out2 = __SXTB16(__ROR(inA, 8));
+        *out1 = __SXTB16(inA);
+#else
+        *out1 = __SXTB16(__ROR(inA, 8));
+        *out2 = __SXTB16(inA);
+#endif
+
+        return source;
+}
+#endif
+
+/**
+ * @defgroup NNBasicMath Basic Math Functions for Neural Network Computation
+ *
+ * Basic Math Functions for Neural Network Computation
+ *
+ */
+
+/**
+ * @brief           Q7 vector multiplication with variable output shifts
+ * @param[in]       *pSrcA        pointer to the first input vector
+ * @param[in]       *pSrcB        pointer to the second input vector
+ * @param[out]      *pDst         pointer to the output vector
+ * @param[in]       out_shift     amount of right-shift for output
+ * @param[in]       blockSize     number of samples in each vector
+ * @return none.
+ *
+ * <b>Scaling and Overflow Behavior:</b>
+ * \par
+ * The function uses saturating arithmetic.
+ * Results outside of the allowable Q15 range [0x8000 0x7FFF] will be saturated.
+ */
+
+void arm_nn_mult_q15(
+  q15_t * pSrcA,
+  q15_t * pSrcB,
+  q15_t * pDst,
+  const uint16_t out_shift,
+  uint32_t blockSize);
+  
+/**
+ * @brief           Q7 vector multiplication with variable output shifts
+ * @param[in]       *pSrcA        pointer to the first input vector
+ * @param[in]       *pSrcB        pointer to the second input vector
+ * @param[out]      *pDst         pointer to the output vector
+ * @param[in]       out_shift     amount of right-shift for output
+ * @param[in]       blockSize     number of samples in each vector
+ * @return none.
+ *
+ * <b>Scaling and Overflow Behavior:</b>
+ * \par
+ * The function uses saturating arithmetic.
+ * Results outside of the allowable Q7 range [0x80 0x7F] will be saturated.
+ */
+
+void arm_nn_mult_q7(
+  q7_t * pSrcA,
+  q7_t * pSrcB,
+  q7_t * pDst,
+  const uint16_t out_shift,
+  uint32_t blockSize);
+ 
+/**
+ * @brief defition to adding rouding offset
+ */
+#ifndef ARM_NN_TRUNCATE
+    #define NN_ROUND(out_shift) ( 0x1 << (out_shift - 1) )
+#else
+    #define NN_ROUND(out_shift) 0
+#endif
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/RTE/_ARMCM0/RTE_Components.h

@@ -0,0 +1,20 @@
+
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ *      *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_cifar10' 
+ * Target:  'ARMCM0' 
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File: 
+ */
+#define CMSIS_device_header "ARMCM0.h"
+
+
+#endif /* RTE_COMPONENTS_H */

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/RTE/_ARMCM3/RTE_Components.h

@@ -0,0 +1,26 @@
+
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ *      *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_nn_test' 
+ * Target:  'ARMCM3' 
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File: 
+ */
+#define CMSIS_device_header "ARMCM3.h"
+
+#define RTE_Compiler_IO_STDERR          /* Compiler I/O: STDERR */
+          #define RTE_Compiler_IO_STDERR_ITM      /* Compiler I/O: STDERR ITM */
+#define RTE_Compiler_IO_STDOUT          /* Compiler I/O: STDOUT */
+          #define RTE_Compiler_IO_STDOUT_ITM      /* Compiler I/O: STDOUT ITM */
+#define RTE_Compiler_IO_TTY             /* Compiler I/O: TTY */
+          #define RTE_Compiler_IO_TTY_ITM         /* Compiler I/O: TTY ITM */
+
+#endif /* RTE_COMPONENTS_H */

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/RTE/_ARMCM4_FP/RTE_Components.h

@@ -0,0 +1,26 @@
+
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ *      *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_nn_test' 
+ * Target:  'ARMCM4_FP' 
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File: 
+ */
+#define CMSIS_device_header "ARMCM4_FP.h"
+
+#define RTE_Compiler_IO_STDERR          /* Compiler I/O: STDERR */
+          #define RTE_Compiler_IO_STDERR_ITM      /* Compiler I/O: STDERR ITM */
+#define RTE_Compiler_IO_STDOUT          /* Compiler I/O: STDOUT */
+          #define RTE_Compiler_IO_STDOUT_ITM      /* Compiler I/O: STDOUT ITM */
+#define RTE_Compiler_IO_TTY             /* Compiler I/O: TTY */
+          #define RTE_Compiler_IO_TTY_ITM         /* Compiler I/O: TTY ITM */
+
+#endif /* RTE_COMPONENTS_H */

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/RTE/_ARMCM7_SP/RTE_Components.h

@@ -0,0 +1,26 @@
+
+/*
+ * Auto generated Run-Time-Environment Component Configuration File
+ *      *** Do not modify ! ***
+ *
+ * Project: 'arm_nnexamples_nn_test' 
+ * Target:  'ARMCM7_SP' 
+ */
+
+#ifndef RTE_COMPONENTS_H
+#define RTE_COMPONENTS_H
+
+
+/*
+ * Define the Device Header File: 
+ */
+#define CMSIS_device_header "ARMCM7_SP.h"
+
+#define RTE_Compiler_IO_STDERR          /* Compiler I/O: STDERR */
+          #define RTE_Compiler_IO_STDERR_ITM      /* Compiler I/O: STDERR ITM */
+#define RTE_Compiler_IO_STDOUT          /* Compiler I/O: STDOUT */
+          #define RTE_Compiler_IO_STDOUT_ITM      /* Compiler I/O: STDOUT ITM */
+#define RTE_Compiler_IO_TTY             /* Compiler I/O: TTY */
+          #define RTE_Compiler_IO_TTY_ITM         /* Compiler I/O: TTY ITM */
+
+#endif /* RTE_COMPONENTS_H */

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_convolve_HWC_q15_ref.c

@@ -0,0 +1,71 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_convolve_HWC_q15_ref(const q15_t * Im_in,  // input image
+                              const uint16_t dim_im_in, // input image dimention
+                              const uint16_t ch_im_in,  // number of input image channels
+                              const q15_t * wt, // kernel weights 
+                              const uint16_t ch_im_out, // number of filters, i.e., output image channels
+                              const uint16_t dim_kernel,    // filter kernel size
+                              const uint16_t padding,   // padding sizes
+                              const uint16_t stride,    // stride
+                              const q15_t * bias,   // bias
+                              const uint16_t bias_shift, const uint16_t out_shift, q15_t * Im_out,  // output image
+                              const uint16_t dim_im_out,    // output image dimension
+                              q15_t * bufferA,  //buffer space for input
+                              q7_t * bufferB    //buffer space for output
+    )
+{
+    int       i, j, k, l, m, n;
+    int       conv_out;
+    int       in_row, in_col;
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out; j++)
+        {
+            for (k = 0; k < dim_im_out; k++)
+            {
+#ifndef ARM_NN_TRUNCATE
+                conv_out = (bias[i] << bias_shift) + (0x1 << (out_shift - 1));
+#else
+                conv_out = bias[i] << bias_shift;
+#endif
+                for (m = 0; m < dim_kernel; m++)
+                {
+                    for (n = 0; n < dim_kernel; n++)
+                    {
+                        in_row = stride * j + m - padding;
+                        in_col = stride * k + n - padding;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out += Im_in[(in_row * dim_im_in + in_col) * ch_im_in + l] *
+                                    wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel + n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16);
+            }
+        }
+    }
+}

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_convolve_HWC_q15_ref_nonsquare.c

@@ -0,0 +1,83 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void
+arm_convolve_HWC_q15_nonsquare_ref(const q15_t * Im_in,
+                          const uint16_t dim_im_in_x,
+                          const uint16_t dim_im_in_y,
+                          const uint16_t ch_im_in,
+                          const q15_t * wt,
+                          const uint16_t ch_im_out,
+                          const uint16_t dim_kernel_x,
+                          const uint16_t dim_kernel_y,
+                          const uint16_t padding_x,
+                          const uint16_t padding_y,
+                          const uint16_t stride_x,
+                          const uint16_t stride_y,
+                          const q15_t * bias,
+                          const uint16_t bias_shift,
+                          const uint16_t out_shift,
+                          q15_t * Im_out,
+                          const uint16_t dim_im_out_x,
+                          const uint16_t dim_im_out_y, 
+                          q15_t * bufferA, 
+                          q7_t * bufferB)
+
+{	
+    uint16_t  i, j, k, l, m, n;
+    int       conv_out;
+    signed char in_row, in_col;
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out_y; j++)
+        {
+            for (k = 0; k < dim_im_out_x; k++)
+            {
+#ifndef ARM_NN_TRUNCATE
+                conv_out = (bias[i] << bias_shift) + (0x1 << (out_shift - 1));
+#else
+                conv_out = bias[i] << bias_shift;
+#endif
+                for (m = 0; m < dim_kernel_y; m++)
+                {
+                    for (n = 0; n < dim_kernel_x; n++)
+                    {
+                        in_row = stride_y * j + m - padding_y;
+                        in_col = stride_x * k + n - padding_x;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out +=
+                                    Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in +
+                                          l] * wt[i * ch_im_in * dim_kernel_x * dim_kernel_y + (m * dim_kernel_x +
+                                                                                            n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16);
+            }
+        }
+    }
+}	
+
+	

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_convolve_HWC_q7_ref.c

@@ -0,0 +1,72 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_convolve_HWC_q7_ref(const q7_t * Im_in,    // input image
+                             const uint16_t dim_im_in,  // input image dimention
+                             const uint16_t ch_im_in,   // number of input image channels
+                             const q7_t * wt,   // kernel weights 
+                             const uint16_t ch_im_out,  // number of filters, i.e., output image channels
+                             const uint16_t dim_kernel, // filter kernel size
+                             const uint16_t padding,    // padding sizes
+                             const uint16_t stride, // stride
+                             const q7_t * bias, // bias
+                             const uint16_t bias_shift, const uint16_t out_shift, q7_t * Im_out,    // output image
+                             const uint16_t dim_im_out, // output image dimension
+                             q15_t * bufferA,   //buffer space for input
+                             q7_t * bufferB //buffer space for output
+    )
+{
+    int       i, j, k, l, m, n;
+    int       conv_out;
+    int       in_row, in_col;
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out; j++)
+        {
+            for (k = 0; k < dim_im_out; k++)
+            {
+#ifndef ARM_NN_TRUNCATE
+                conv_out = ((q31_t) (bias[i]) << bias_shift) + (0x1 << (out_shift - 1));
+#else
+                conv_out = bias[i] << bias_shift;
+#endif
+                for (m = 0; m < dim_kernel; m++)
+                {
+                    for (n = 0; n < dim_kernel; n++)
+                    {
+                        // if-for implementation
+                        in_row = stride * j + m - padding;
+                        in_col = stride * k + n - padding;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out += Im_in[(in_row * dim_im_in + in_col) * ch_im_in + l] *
+                                    wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel + n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+}

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_convolve_HWC_q7_ref_nonsquare.c

@@ -0,0 +1,78 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_convolve_HWC_q7_ref_nonsquare(const q7_t * Im_in,  // input image
+                                       const uint16_t dim_im_in_x,  // input image dimention x
+                                       const uint16_t dim_im_in_y,  // input image dimention y
+                                       const uint16_t ch_im_in, // number of input image channels
+                                       const q7_t * wt, // kernel weights 
+                                       const uint16_t ch_im_out,    // number of filters, i.e., output image channels
+                                       const uint16_t dim_kernel_x, // filter kernel size x
+                                       const uint16_t dim_kernel_y, // filter kernel size y
+                                       const uint16_t padding_x,    // padding sizes x
+                                       const uint16_t padding_y,    // padding sizes y
+                                       const uint16_t stride_x, // stride x
+                                       const uint16_t stride_y, // stride y
+                                       const q7_t * bias,   // bias
+                                       const uint16_t bias_shift, const uint16_t out_shift, q7_t * Im_out,  // output image
+                                       const uint16_t dim_im_out_x, // output image dimension x
+                                       const uint16_t dim_im_out_y, // output image dimension y
+                                       q15_t * bufferA, //buffer space for input
+                                       q7_t * bufferB   //buffer space for output
+    )
+{
+    int       i, j, k, l, m, n;
+    int       conv_out;
+    int       in_row, in_col;
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out_y; j++)
+        {
+            for (k = 0; k < dim_im_out_x; k++)
+            {
+#ifndef ARM_NN_TRUNCATE
+                conv_out = ((q31_t) (bias[i]) << bias_shift) + (0x1 << (out_shift - 1));
+#else
+                conv_out = bias[i] << bias_shift;
+#endif
+                for (m = 0; m < dim_kernel_y; m++)
+                {
+                    for (n = 0; n < dim_kernel_x; n++)
+                    {
+                        // if-for implementation
+                        in_row = stride_y * j + m - padding_y;
+                        in_col = stride_x * k + n - padding_x;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] *
+                                    wt[i * ch_im_in * dim_kernel_y * dim_kernel_x + (m * dim_kernel_x + n) * ch_im_in +
+                                       l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+}

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_depthwise_separable_conv_HWC_q7_ref.c

@@ -0,0 +1,70 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_depthwise_separable_conv_HWC_q7_ref(const q7_t * Im_in,    // input image
+                                             const uint16_t dim_im_in,  // input image dimention
+                                             const uint16_t ch_im_in,   // number of input image channels
+                                             const q7_t * wt,   // kernel weights 
+                                             const uint16_t ch_im_out,  // number of filters, i.e., output image channels
+                                             const uint16_t dim_kernel, // filter kernel size
+                                             const uint16_t padding,    // padding sizes
+                                             const uint16_t stride, // stride
+                                             const q7_t * bias, // bias
+                                             const uint16_t bias_shift, // amount of left-shift for bias
+                                             const uint16_t out_shift,  // amount of right-shift for output
+                                             q7_t * Im_out, // output image
+                                             const uint16_t dim_im_out, // output image dimension
+                                             q15_t * bufferA,   //buffer space for input
+                                             q7_t * bufferB //buffer space for output
+    )
+{
+    int       i_out_y, i_out_x, i_ch_out;
+    int       i_ker_y, i_ker_x;
+    for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+        {
+            for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++)
+            {
+                // for each output
+#ifndef ARM_NN_TRUNCATE
+                int       conv_out = (bias[i_ch_out] << bias_shift) + (0x1 << (out_shift - 1));
+#else
+                int       conv_out = bias[i_ch_out] << bias_shift;
+#endif
+                for (i_ker_y = 0; i_ker_y < dim_kernel; i_ker_y++)
+                {
+                    for (i_ker_x = 0; i_ker_x < dim_kernel; i_ker_x++)
+                    {
+                        int       in_row = stride * i_out_y + i_ker_y - padding;
+                        int       in_col = stride * i_out_x + i_ker_x - padding;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+                        {
+                            conv_out += Im_in[(in_row * dim_im_in + in_col) * ch_im_in + i_ch_out] *
+                                wt[(i_ker_y * dim_kernel + i_ker_x) * ch_im_out + i_ch_out];
+                        }
+                    }
+                }
+                Im_out[(i_out_y * dim_im_out + i_out_x) * ch_im_out + i_ch_out] =
+                    (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+}

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_depthwise_separable_conv_HWC_q7_ref_nonsquare.c

@@ -0,0 +1,75 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_depthwise_separable_conv_HWC_q7_ref_nonsquare(const q7_t * Im_in,  // input image
+                                                       const uint16_t dim_im_in_x,  // input image dimention x
+                                                       const uint16_t dim_im_in_y,  // input image dimention y
+                                                       const uint16_t ch_im_in, // number of input image channels
+                                                       const q7_t * wt, // kernel weights 
+                                                       const uint16_t ch_im_out,    // number of filters, i.e., output image channels
+                                                       const uint16_t dim_kernel_x, // filter kernel size x
+                                                       const uint16_t dim_kernel_y, // filter kernel size y
+                                                       const uint16_t padding_x,    // padding sizes x
+                                                       const uint16_t padding_y,    // padding sizes y
+                                                       const uint16_t stride_x, // stride x
+                                                       const uint16_t stride_y, // stride y
+                                                       const q7_t * bias,   // bias
+                                                       const uint16_t bias_shift,   // amount of left-shift for bias
+                                                       const uint16_t out_shift,    // amount of right-shift for output
+                                                       q7_t * Im_out,   // output image
+                                                       const uint16_t dim_im_out_x, // output image dimension x
+                                                       const uint16_t dim_im_out_y, // output image dimension y
+                                                       q15_t * bufferA, //buffer space for input
+                                                       q7_t * bufferB   //buffer space for output
+    )
+{
+    int       i_out_y, i_out_x, i_ch_out;
+    int       i_ker_y, i_ker_x;
+    for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+        {
+            for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++)
+            {
+                // for each output
+#ifndef ARM_NN_TRUNCATE
+                int       conv_out = (bias[i_ch_out] << bias_shift) + (0x1 << (out_shift - 1));
+#else
+                int       conv_out = bias[i_ch_out] << bias_shift;
+#endif
+                for (i_ker_y = 0; i_ker_y < dim_kernel_y; i_ker_y++)
+                {
+                    for (i_ker_x = 0; i_ker_x < dim_kernel_x; i_ker_x++)
+                    {
+                        int       in_row = stride_y * i_out_y + i_ker_y - padding_y;
+                        int       in_col = stride_x * i_out_x + i_ker_x - padding_x;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+                        {
+                            conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + i_ch_out] *
+                                wt[(i_ker_y * dim_kernel_x + i_ker_x) * ch_im_out + i_ch_out];
+                        }
+                    }
+                }
+                Im_out[(i_out_y * dim_im_out_x + i_out_x) * ch_im_out + i_ch_out] =
+                    (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+}

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_mat_q7_vec_q15_opt_ref.c

@@ -0,0 +1,120 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_fully_connected_mat_q7_vec_q15_opt_ref(const q15_t * pV,   // pointer to vector
+                                                const q7_t * pM,    // pointer to matrix
+                                                const uint16_t dim_vec, // length of the vector
+                                                const uint16_t num_of_rows, // numCol of A
+                                                const uint16_t bias_shift,  // amount of left-shift for bias
+                                                const uint16_t out_shift,   // amount of right-shift for output
+                                                const q7_t * bias, q15_t * pOut,    // output operand
+                                                q15_t * vec_buffer)
+{
+
+    uint16_t  rowCnt = num_of_rows >> 2;
+    const q7_t *pB = pM;
+    const q15_t *pA;
+    q15_t    *pO = pOut;
+    const q7_t *pBias = bias;
+
+    while (rowCnt)
+    {
+        pA = pV;
+#ifndef ARM_NN_TRUNCATE
+        q31_t     sum = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+        q31_t     sum2 = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+        q31_t     sum3 = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+        q31_t     sum4 = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+#else
+        q31_t     sum = *pBias++ << bias_shift;
+        q31_t     sum2 = *pBias++ << bias_shift;
+        q31_t     sum3 = *pBias++ << bias_shift;
+        q31_t     sum4 = *pBias++ << bias_shift;
+#endif
+
+        uint16_t  colCnt = dim_vec >> 1;
+
+        while (colCnt)
+        {
+            q15_t     inA1 = *pA++;
+            q15_t     inA2 = *pA++;
+
+            q7_t      inB1 = *pB++;
+            q7_t      inB3 = *pB++;
+            q7_t      inB2 = *pB++;
+            q7_t      inB4 = *pB++;
+
+            sum += inA1 * inB1 + inA2 * inB2;
+            sum2 += inA1 * inB3 + inA2 * inB4;
+
+            inB1 = *pB++;
+            inB3 = *pB++;
+            inB2 = *pB++;
+            inB4 = *pB++;
+
+            sum3 += inA1 * inB1 + inA2 * inB2;
+            sum4 += inA1 * inB3 + inA2 * inB4;
+
+            colCnt--;
+        }
+        colCnt = dim_vec & 0x1;
+        while (colCnt)
+        {
+            q15_t     inA = *pA++;
+            q7_t      inB = *pB++;
+            sum += inA * inB;
+            inB = *pB++;
+            sum2 += inA * inB;
+            inB = *pB++;
+            sum3 += inA * inB;
+            inB = *pB++;
+            sum4 += inA * inB;
+
+            colCnt--;
+        }
+        *pO++ = (q15_t) __SSAT((sum >> out_shift), 16);
+        *pO++ = (q15_t) __SSAT((sum2 >> out_shift), 16);
+        *pO++ = (q15_t) __SSAT((sum3 >> out_shift), 16);
+        *pO++ = (q15_t) __SSAT((sum4 >> out_shift), 16);
+
+        rowCnt--;
+    }
+
+    rowCnt = num_of_rows & 0x3;
+
+    while (rowCnt)
+    {
+        pA = pV;
+#ifndef ARM_NN_TRUNCATE
+        int       ip_out = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+#else
+        int       ip_out = *pBias++ << bias_shift;
+#endif
+        for (int j = 0; j < dim_vec; j++)
+        {
+            q15_t     inA = *pA++;
+            q7_t      inB = *pB++;
+            ip_out += inA * inB;
+        }
+        *pO++ = (q15_t) __SSAT((ip_out >> out_shift), 16);
+
+        rowCnt--;
+    }
+}

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_mat_q7_vec_q15_ref.c

@@ -0,0 +1,43 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_fully_connected_mat_q7_vec_q15_ref(const q15_t * pV,   // pointer to vector
+                                            const q7_t * pM,    // pointer to matrix
+                                            const uint16_t dim_vec, // length of the vector
+                                            const uint16_t num_of_rows, // numCol of A
+                                            const uint16_t bias_shift,  // amount of left-shift for bias
+                                            const uint16_t out_shift,   // amount of right-shift for output
+                                            const q7_t * bias, q15_t * pOut,    // output operand
+                                            q15_t * vec_buffer)
+{
+    for (int i = 0; i < num_of_rows; i++)
+    {
+#ifndef ARM_NN_TRUNCATE
+        int       ip_out = (bias[i] << bias_shift) + (0x1 << (out_shift - 1));
+#else
+        int       ip_out = bias[i] << bias_shift;
+#endif
+        for (int j = 0; j < dim_vec; j++)
+        {
+            ip_out += pV[j] * pM[i * dim_vec + j];
+        }
+        pOut[i] = (q15_t) __SSAT((ip_out >> out_shift), 16);
+    }
+}

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_q15_opt_ref.c

@@ -0,0 +1,119 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_fully_connected_q15_opt_ref(const q15_t * pV,  // pointer to vector
+                                     const q15_t * pM,  // pointer to matrix
+                                     const uint16_t dim_vec,    // length of the vector
+                                     const uint16_t num_of_rows,    // numCol of A
+                                     const uint16_t bias_shift, // amount of left-shift for bias
+                                     const uint16_t out_shift,  // amount of right-shift for output
+                                     const q15_t * bias, q15_t * pOut,  // output operand
+                                     q15_t * vec_buffer)
+{
+
+    uint16_t  rowCnt = num_of_rows >> 2;
+    const q15_t *pB = pM;
+    const q15_t *pA;
+    q15_t    *pO = pOut;
+    const q15_t *pBias = bias;
+
+    while (rowCnt)
+    {
+        pA = pV;
+#ifndef ARM_NN_TRUNCATE
+        q31_t     sum = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+        q31_t     sum2 = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+        q31_t     sum3 = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+        q31_t     sum4 = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+#else
+        q31_t     sum = *pBias++ << bias_shift;
+        q31_t     sum2 = *pBias++ << bias_shift;
+        q31_t     sum3 = *pBias++ << bias_shift;
+        q31_t     sum4 = *pBias++ << bias_shift;
+#endif
+
+        uint16_t  colCnt = dim_vec >> 1;
+
+        while (colCnt)
+        {
+            q15_t     inA1 = *pA++;
+            q15_t     inA2 = *pA++;
+
+            q15_t     inB1 = *pB++;
+            q15_t     inB2 = *pB++;
+            sum += inA1 * inB1 + inA2 * inB2;
+
+            inB1 = *pB++;
+            inB2 = *pB++;
+            sum2 += inA1 * inB1 + inA2 * inB2;
+
+            inB1 = *pB++;
+            inB2 = *pB++;
+            sum3 += inA1 * inB1 + inA2 * inB2;
+
+            inB1 = *pB++;
+            inB2 = *pB++;
+            sum4 += inA1 * inB1 + inA2 * inB2;
+
+            colCnt--;
+        }
+        colCnt = dim_vec & 0x1;
+        while (colCnt)
+        {
+            q15_t     inA = *pA++;
+            q15_t     inB = *pB++;
+            sum += inA * inB;
+            inB = *pB++;
+            sum2 += inA * inB;
+            inB = *pB++;
+            sum3 += inA * inB;
+            inB = *pB++;
+            sum4 += inA * inB;
+            colCnt--;
+        }
+        *pO++ = (q15_t) __SSAT((sum >> out_shift), 16);
+        *pO++ = (q15_t) __SSAT((sum2 >> out_shift), 16);
+        *pO++ = (q15_t) __SSAT((sum3 >> out_shift), 16);
+        *pO++ = (q15_t) __SSAT((sum4 >> out_shift), 16);
+
+        rowCnt--;
+    }
+
+    rowCnt = num_of_rows & 0x3;
+
+    while (rowCnt)
+    {
+        pA = pV;
+#ifndef ARM_NN_TRUNCATE
+        int       ip_out = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+#else
+        int       ip_out = *pBias++ << bias_shift;
+#endif
+        for (int j = 0; j < dim_vec; j++)
+        {
+            q15_t     inA = *pA++;
+            q15_t     inB = *pB++;
+            ip_out += inA * inB;
+        }
+        *pO++ = (q15_t) __SSAT((ip_out >> out_shift), 16);
+
+        rowCnt--;
+    }
+}

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_q15_ref.c

@@ -0,0 +1,43 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_fully_connected_q15_ref(const q15_t * pV,  // pointer to vector
+                                 const q15_t * pM,  // pointer to matrix
+                                 const uint16_t dim_vec,    // length of the vector
+                                 const uint16_t num_of_rows,    // numCol of A
+                                 const uint16_t bias_shift, // amount of left-shift for bias
+                                 const uint16_t out_shift,  // amount of right-shift for output
+                                 const q15_t * bias, q15_t * pOut,  // output operand
+                                 q15_t * vec_buffer)
+{
+    for (int i = 0; i < num_of_rows; i++)
+    {
+#ifndef ARM_NN_TRUNCATE
+        int       ip_out = (bias[i] << bias_shift) + (0x1 << (out_shift - 1));
+#else
+        int       ip_out = bias[i] << bias_shift;
+#endif
+        for (int j = 0; j < dim_vec; j++)
+        {
+            ip_out += pV[j] * pM[i * dim_vec + j];
+        }
+        pOut[i] = (q15_t) __SSAT((ip_out >> out_shift), 16);
+    }
+}

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_q7_opt_ref.c

@@ -0,0 +1,138 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_fully_connected_q7_opt_ref(const q7_t * pV,    // pointer to vector
+                                    const q7_t * pM,    // pointer to matrix
+                                    const uint16_t dim_vec, // length of the vector
+                                    const uint16_t num_of_rows, // numCol of A
+                                    const uint16_t bias_shift,  // amount of left-shift for bias
+                                    const uint16_t out_shift,   // amount of right-shift for output
+                                    const q7_t * bias, q7_t * pOut, // output operand
+                                    q15_t * vec_buffer)
+{
+
+    uint16_t  rowCnt = num_of_rows >> 2;
+    const q7_t *pB = pM;
+    const q7_t *pA;
+    q7_t     *pO = pOut;
+    const q7_t *pBias = bias;
+
+    while (rowCnt)
+    {
+        pA = pV;
+#ifndef ARM_NN_TRUNCATE
+        q31_t     sum = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+        q31_t     sum2 = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+        q31_t     sum3 = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+        q31_t     sum4 = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+#else
+        q31_t     sum = *pBias++ << bias_shift;
+        q31_t     sum2 = *pBias++ << bias_shift;
+        q31_t     sum3 = *pBias++ << bias_shift;
+        q31_t     sum4 = *pBias++ << bias_shift;
+#endif
+
+        uint16_t  colCnt = dim_vec >> 2;
+
+        while (colCnt)
+        {
+            q7_t      inA1 = *pA++;
+            q7_t      inA3 = *pA++;
+            q7_t      inA2 = *pA++;
+            q7_t      inA4 = *pA++;
+
+            q7_t      inB1 = *pB++;
+            q7_t      inB3 = *pB++;
+            q7_t      inB2 = *pB++;
+            q7_t      inB4 = *pB++;
+
+            sum += inA1 * inB1 + inA2 * inB2;
+            sum2 += inA1 * inB3 + inA2 * inB4;
+
+            inB1 = *pB++;
+            inB3 = *pB++;
+            inB2 = *pB++;
+            inB4 = *pB++;
+
+            sum3 += inA1 * inB1 + inA2 * inB2;
+            sum4 += inA1 * inB3 + inA2 * inB4;
+
+            inB1 = *pB++;
+            inB3 = *pB++;
+            inB2 = *pB++;
+            inB4 = *pB++;
+
+            sum += inA3 * inB1 + inA4 * inB2;
+            sum2 += inA3 * inB3 + inA4 * inB4;
+
+            inB1 = *pB++;
+            inB3 = *pB++;
+            inB2 = *pB++;
+            inB4 = *pB++;
+
+            sum3 += inA3 * inB1 + inA4 * inB2;
+            sum4 += inA3 * inB3 + inA4 * inB4;
+
+            colCnt--;
+        }
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q7_t      inA = *pA++;
+            q7_t      inB = *pB++;
+            sum += inA * inB;
+            inB = *pB++;
+            sum2 += inA * inB;
+            inB = *pB++;
+            sum3 += inA * inB;
+            inB = *pB++;
+            sum4 += inA * inB;
+
+            colCnt--;
+        }
+        *pO++ = (q7_t) __SSAT((sum >> out_shift), 8);
+        *pO++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+        *pO++ = (q7_t) __SSAT((sum3 >> out_shift), 8);
+        *pO++ = (q7_t) __SSAT((sum4 >> out_shift), 8);
+
+        rowCnt--;
+    }
+
+    rowCnt = num_of_rows & 0x3;
+
+    while (rowCnt)
+    {
+        pA = pV;
+#ifndef ARM_NN_TRUNCATE
+        int       ip_out = (*pBias++ << bias_shift) + (0x1 << (out_shift - 1));
+#else
+        int       ip_out = *pBias++ << bias_shift;
+#endif
+        for (int j = 0; j < dim_vec; j++)
+        {
+            q7_t      inA = *pA++;
+            q7_t      inB = *pB++;
+            ip_out += inA * inB;
+        }
+        *pO++ = (q7_t) __SSAT((ip_out >> out_shift), 8);
+
+        rowCnt--;
+    }
+}

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_fully_connected_q7_ref.c

@@ -0,0 +1,43 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_fully_connected_q7_ref(const q7_t * pV,    // pointer to vector
+                                const q7_t * pM,    // pointer to matrix
+                                const uint16_t dim_vec, // length of the vector
+                                const uint16_t num_of_rows, // numCol of A
+                                const uint16_t bias_shift,  // amount of left-shift for bias
+                                const uint16_t out_shift,   // amount of right-shift for output
+                                const q7_t * bias, q7_t * pOut, // output operand
+                                q15_t * vec_buffer)
+{
+    for (int i = 0; i < num_of_rows; i++)
+    {
+#ifndef ARM_NN_TRUNCATE
+        int       ip_out = (bias[i] << bias_shift) + (0x1 << (out_shift - 1));
+#else
+        int       ip_out = bias[i] << bias_shift;
+#endif
+        for (int j = 0; j < dim_vec; j++)
+        {
+            ip_out += pV[j] * pM[i * dim_vec + j];
+        }
+        pOut[i] = (q7_t) __SSAT((ip_out >> out_shift), 8);
+    }
+}

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_nn_mult_ref.c

@@ -0,0 +1,58 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+void      arm_nn_mult_q7_ref(q7_t * pSrcA, 
+                             q7_t * pSrcB, 
+                             q7_t * pDst, 
+                             const uint16_t out_shift, 
+                             uint32_t blockSize) {
+    uint16_t  i;
+
+for (i = 0; i < blockSize; i++)
+    {
+		q31_t product = pSrcA[i] * pSrcB[i];
+#ifndef ARM_NN_TRUNCATE
+        pDst[i] = (q7_t)__SSAT((product + (0x1 << (out_shift - 1)))>>out_shift, 8);
+#else
+        pDst[i] = (q7_t)__SSAT(product >> out_shift, 8);
+#endif
+    }
+}
+
+void     arm_nn_mult_q15_ref(q15_t * pSrcA, 
+                             q15_t * pSrcB, 
+                             q15_t * pDst, 
+                             const uint16_t out_shift, 
+                             uint32_t blockSize) {
+    uint16_t  i;
+
+for (i = 0; i < blockSize; i++)
+    {
+		q31_t product = pSrcA[i] * pSrcB[i];
+#ifndef ARM_NN_TRUNCATE
+        pDst[i] = (q15_t)__SSAT((product + (0x1 << (out_shift - 1)))>>out_shift, 16);
+#else
+        pDst[i] = (q15_t)__SSAT(product >> out_shift, 16);
+#endif
+
+
+    }
+}

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_pool_ref.c

@@ -0,0 +1,96 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ref_functions.h"
+
+void arm_avepool_q7_HWC_ref(const q7_t * Im_in, // input image
+                            const uint16_t dim_im_in,   // input image dimension
+                            const uint16_t ch_im_in,    // number of input image channels
+                            const uint16_t dim_kernel,  // window kernel size
+                            const uint16_t padding, // padding sizes
+                            const uint16_t stride,  // stride
+                            const uint16_t dim_im_out,  // output image dimension
+                            q7_t * bufferA, // a buffer for local storage
+                            q7_t * Im_out)
+{
+    int16_t   i_ch_in, i_x, i_y;
+    int16_t   k_x, k_y;
+
+    for (i_ch_in = 0; i_ch_in < ch_im_in; i_ch_in++)
+    {
+        for (i_y = 0; i_y < dim_im_out; i_y++)
+        {
+            for (i_x = 0; i_x < dim_im_out; i_x++)
+            {
+                int       sum = 0;
+                int       count = 0;
+                for (k_y = i_y * stride - padding; k_y < i_y * stride - padding + dim_kernel; k_y++)
+                {
+                    for (k_x = i_x * stride - padding; k_x < i_x * stride - padding + dim_kernel; k_x++)
+                    {
+                        if (k_y >= 0 && k_x >= 0 && k_y < dim_im_in && k_x < dim_im_in)
+                        {
+                            sum += Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)];
+                            count++;
+                        }
+                    }
+                }
+                Im_out[i_ch_in + ch_im_in * (i_x + i_y * dim_im_out)] = sum / count;
+            }
+        }
+    }
+}
+
+void arm_maxpool_q7_HWC_ref(const q7_t * Im_in, // input image
+                            const uint16_t dim_im_in,   // input image dimension
+                            const uint16_t ch_im_in,    // number of input image channels
+                            const uint16_t dim_kernel,  // window kernel size
+                            const uint16_t padding, // padding sizes
+                            const uint16_t stride,  // stride
+                            const uint16_t dim_im_out,  // output image dimension
+                            q7_t * bufferA, // a buffer for local storage
+                            q7_t * Im_out)
+{
+    int16_t   i_ch_in, i_x, i_y;
+    int16_t   k_x, k_y;
+
+    for (i_ch_in = 0; i_ch_in < ch_im_in; i_ch_in++)
+    {
+        for (i_y = 0; i_y < dim_im_out; i_y++)
+        {
+            for (i_x = 0; i_x < dim_im_out; i_x++)
+            {
+                int       max = -129;
+                for (k_y = i_y * stride - padding; k_y < i_y * stride - padding + dim_kernel; k_y++)
+                {
+                    for (k_x = i_x * stride - padding; k_x < i_x * stride - padding + dim_kernel; k_x++)
+                    {
+                        if (k_y >= 0 && k_x >= 0 && k_y < dim_im_in && k_x < dim_im_in)
+                        {
+                            if (Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)] > max)
+                            {
+                                max = Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)];
+                            }
+                        }
+                    }
+                }
+                Im_out[i_ch_in + ch_im_in * (i_x + i_y * dim_im_out)] = max;
+            }
+        }
+    }
+}

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/arm_relu_ref.c

@@ -0,0 +1,42 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+void arm_relu_q7_ref(q7_t * data, uint16_t size)
+{
+    uint16_t  i;
+
+    for (i = 0; i < size; i++)
+    {
+        if (data[i] < 0)
+            data[i] = 0;
+    }
+}
+
+void arm_relu_q15_ref(q15_t * data, uint16_t size)
+{
+    uint16_t  i;
+
+    for (i = 0; i < size; i++)
+    {
+        if (data[i] < 0)
+            data[i] = 0;
+    }
+}

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/Ref_Implementations/ref_functions.h

@@ -0,0 +1,250 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef _REF_FUNCTIONS_H_
+#define _REF_FUNCTIONS_H_
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+//#include "arm_nnsupportfunctions.h"
+#include "fully_connected_testing_weights.h"
+
+#ifdef __cplusplus
+extern    "C"
+{
+#endif
+
+/*
+ *
+ * Convolution reference implemenation
+ *
+ */
+
+    void      arm_convolve_HWC_q7_ref(const q7_t * Im_in,   // input image
+                                      const uint16_t dim_im_in, // input image dimention
+                                      const uint16_t ch_im_in,  // number of input image channels
+                                      const q7_t * wt,  // kernel weights 
+                                      const uint16_t ch_im_out, // number of filters, i.e., output image channels
+                                      const uint16_t dim_kernel,    // filter kernel size
+                                      const uint16_t padding,   // padding sizes
+                                      const uint16_t stride,    // stride
+                                      const q7_t * bias,    // bias
+                                      const uint16_t bias_shift, const uint16_t out_shift, q7_t * Im_out,   // output image
+                                      const uint16_t dim_im_out,    // output image dimension
+                                      q15_t * bufferA,  //buffer space for input
+                                      q7_t * bufferB    //buffer space for output
+        );
+
+    void      arm_convolve_HWC_q7_ref_nonsquare(const q7_t * Im_in, // input image
+                                                const uint16_t dim_im_in_x, // input image dimention x
+                                                const uint16_t dim_im_in_y, // input image dimention y
+                                                const uint16_t ch_im_in,    // number of input image channels
+                                                const q7_t * wt,    // kernel weights 
+                                                const uint16_t ch_im_out,   // number of filters, i.e., output image channels
+                                                const uint16_t dim_kernel_x,    // filter kernel size x
+                                                const uint16_t dim_kernel_y,    // filter kernel size y
+                                                const uint16_t padding_x,   // padding sizes x
+                                                const uint16_t padding_y,   // padding sizes y
+                                                const uint16_t stride_x,    // stride x
+                                                const uint16_t stride_y,    // stride y
+                                                const q7_t * bias,  // bias
+                                                const uint16_t bias_shift, const uint16_t out_shift, q7_t * Im_out, // output image
+                                                const uint16_t dim_im_out_x,    // output image dimension x
+                                                const uint16_t dim_im_out_y,    // output image dimension y
+                                                q15_t * bufferA,    //buffer space for input
+                                                q7_t * bufferB  //buffer space for output
+        );
+
+    void      arm_convolve_HWC_q15_ref(const q15_t * Im_in, // input image
+                                       const uint16_t dim_im_in,    // input image dimention
+                                       const uint16_t ch_im_in, // number of input image channels
+                                       const q15_t * wt,    // kernel weights 
+                                       const uint16_t ch_im_out,    // number of filters, i.e., output image channels
+                                       const uint16_t dim_kernel,   // filter kernel size
+                                       const uint16_t padding,  // padding sizes
+                                       const uint16_t stride,   // stride
+                                       const q15_t * bias,  // bias
+                                       const uint16_t bias_shift, const uint16_t out_shift, q15_t * Im_out, // output image
+                                       const uint16_t dim_im_out,   // output image dimension
+                                       q15_t * bufferA, //buffer space for input
+                                       q7_t * bufferB   //buffer space for output
+        );
+    void      arm_convolve_HWC_q15_nonsquare_ref(const q15_t * Im_in,
+                                                      const uint16_t dim_im_in_x,
+                                                      const uint16_t dim_im_in_y,
+                                                      const uint16_t ch_im_in,
+                                                      const q15_t * wt,
+                                                      const uint16_t ch_im_out,
+                                                      const uint16_t dim_kernel_x,
+                                                      const uint16_t dim_kernel_y,
+                                                      const uint16_t padding_x,
+                                                      const uint16_t padding_y,
+                                                      const uint16_t stride_x,
+                                                      const uint16_t stride_y,
+                                                      const q15_t * bias,
+                                                      const uint16_t bias_shift,
+                                                      const uint16_t out_shift,
+                                                      q15_t * Im_out,
+                                                      const uint16_t dim_im_out_x,
+                                                      const uint16_t dim_im_out_y, 
+                                                      q15_t * bufferA, 
+                                                      q7_t * bufferB);
+													  
+    void      arm_depthwise_separable_conv_HWC_q7_ref(const q7_t * Im_in,   // input image
+                                                      const uint16_t dim_im_in, // input image dimention
+                                                      const uint16_t ch_im_in,  // number of input image channels
+                                                      const q7_t * wt,  // kernel weights 
+                                                      const uint16_t ch_im_out, // number of filters, i.e., output image channels
+                                                      const uint16_t dim_kernel,    // filter kernel size
+                                                      const uint16_t padding,   // padding sizes
+                                                      const uint16_t stride,    // stride
+                                                      const q7_t * bias,    // bias
+                                                      const uint16_t bias_shift,    // amount of left-shift for bias
+                                                      const uint16_t out_shift, // amount of right-shift for output
+                                                      q7_t * Im_out,    // output image
+                                                      const uint16_t dim_im_out,    // output image dimension
+                                                      q15_t * bufferA,  //buffer space for input
+                                                      q7_t * bufferB    //buffer space for output
+        );
+    void      arm_depthwise_separable_conv_HWC_q7_ref_nonsquare(const q7_t * Im_in, // input image
+                                                                const uint16_t dim_im_in_x, // input image dimention x
+                                                                const uint16_t dim_im_in_y, // input image dimention y
+                                                                const uint16_t ch_im_in,    // number of input image channels
+                                                                const q7_t * wt,    // kernel weights 
+                                                                const uint16_t ch_im_out,   // number of filters, i.e., output image channels
+                                                                const uint16_t dim_kernel_x,    // filter kernel size x
+                                                                const uint16_t dim_kernel_y,    // filter kernel size y
+                                                                const uint16_t padding_x,   // padding sizes x
+                                                                const uint16_t padding_y,   // padding sizes y
+                                                                const uint16_t stride_x,    // stride x
+                                                                const uint16_t stride_y,    // stride y
+                                                                const q7_t * bias,  // bias
+                                                                const uint16_t bias_shift,  // amount of left-shift for bias
+                                                                const uint16_t out_shift,   // amount of right-shift for output
+                                                                q7_t * Im_out,  // output image
+                                                                const uint16_t dim_im_out_x,    // output image dimension x
+                                                                const uint16_t dim_im_out_y,    // output image dimension y
+                                                                q15_t * bufferA,    //buffer space for input
+                                                                q7_t * bufferB  //buffer space for output
+        );
+
+/*
+ *
+ * Fully-connected reference implemenation
+ *
+ */
+
+    void      arm_fully_connected_q7_ref(const q7_t * pV,   // pointer to vector
+                                         const q7_t * pM,   // pointer to matrix
+                                         const uint16_t dim_vec,    // length of the vector
+                                         const uint16_t num_of_rows,    // numCol of A
+                                         const uint16_t bias_shift, // amount of left-shift for bias
+                                         const uint16_t out_shift,  // amount of right-shift for output
+                                         const q7_t * bias, q7_t * pOut,    // output operand
+                                         q15_t * vec_buffer);
+
+    void      arm_fully_connected_q15_ref(const q15_t * pV, // pointer to vector
+                                          const q15_t * pM, // pointer to matrix
+                                          const uint16_t dim_vec,   // length of the vector
+                                          const uint16_t num_of_rows,   // numCol of A
+                                          const uint16_t bias_shift,    // amount of left-shift for bias
+                                          const uint16_t out_shift, // amount of right-shift for output
+                                          const q15_t * bias, q15_t * pOut, // output operand
+                                          q15_t * vec_buffer);
+
+    void      arm_fully_connected_mat_q7_vec_q15_ref(const q15_t * pV,  // pointer to vector
+                                                     const q7_t * pM,   // pointer to matrix
+                                                     const uint16_t dim_vec,    // length of the vector
+                                                     const uint16_t num_of_rows,    // numCol of A
+                                                     const uint16_t bias_shift, // amount of left-shift for bias
+                                                     const uint16_t out_shift,  // amount of right-shift for output
+                                                     const q7_t * bias, q15_t * pOut,   // output operand
+                                                     q15_t * vec_buffer);
+
+    void      arm_fully_connected_q7_opt_ref(const q7_t * pV,   // pointer to vector
+                                             const q7_t * pM,   // pointer to matrix
+                                             const uint16_t dim_vec,    // length of the vector
+                                             const uint16_t num_of_rows,    // numCol of A
+                                             const uint16_t bias_shift, // amount of left-shift for bias
+                                             const uint16_t out_shift,  // amount of right-shift for output
+                                             const q7_t * bias, q7_t * pOut,    // output operand
+                                             q15_t * vec_buffer);
+
+    void      arm_fully_connected_q15_opt_ref(const q15_t * pV, // pointer to vector
+                                              const q15_t * pM, // pointer to matrix
+                                              const uint16_t dim_vec,   // length of the vector
+                                              const uint16_t num_of_rows,   // numCol of A
+                                              const uint16_t bias_shift,    // amount of left-shift for bias
+                                              const uint16_t out_shift, // amount of right-shift for output
+                                              const q15_t * bias, q15_t * pOut, // output operand
+                                              q15_t * vec_buffer);
+
+    void      arm_fully_connected_mat_q7_vec_q15_opt_ref(const q15_t * pV,  // pointer to vector
+                                                         const q7_t * pM,   // pointer to matrix
+                                                         const uint16_t dim_vec,    // length of the vector
+                                                         const uint16_t num_of_rows,    // numCol of A
+                                                         const uint16_t bias_shift, // amount of left-shift for bias
+                                                         const uint16_t out_shift,  // amount of right-shift for output
+                                                         const q7_t * bias, q15_t * pOut,   // output operand
+                                                         q15_t * vec_buffer);
+
+/*
+ *
+ * Pooling reference implemenation
+ *
+ */
+
+    void      arm_avepool_q7_HWC_ref(const q7_t * Im_in,    // input image
+                                     const uint16_t dim_im_in,  // input image dimension
+                                     const uint16_t ch_im_in,   // number of input image channels
+                                     const uint16_t dim_kernel, // window kernel size
+                                     const uint16_t padding,    // padding sizes
+                                     const uint16_t stride, // stride
+                                     const uint16_t dim_im_out, // output image dimension
+                                     q7_t * bufferA,    // a buffer for local storage
+                                     q7_t * Im_out);
+
+    void      arm_maxpool_q7_HWC_ref(const q7_t * Im_in,    // input image
+                                     const uint16_t dim_im_in,  // input image dimension
+                                     const uint16_t ch_im_in,   // number of input image channels
+                                     const uint16_t dim_kernel, // window kernel size
+                                     const uint16_t padding,    // padding sizes
+                                     const uint16_t stride, // stride
+                                     const uint16_t dim_im_out, // output image dimension
+                                     q7_t * bufferA,    // a buffer for local storage
+                                     q7_t * Im_out);
+
+/*
+ *
+ * Other reference implemenation
+ *
+ */
+
+    void      arm_relu_q7_ref(q7_t * data, uint16_t size);
+
+    void      arm_relu_q15_ref(q15_t * data, uint16_t size);
+
+    void      arm_nn_mult_q7_ref(q7_t * pSrcA, q7_t * pSrcB, q7_t * pDst, const uint16_t out_shift, uint32_t blockSize);
+
+    void      arm_nn_mult_q15_ref(q15_t * pSrcA, q15_t * pSrcB, q15_t * pDst, const uint16_t out_shift, uint32_t blockSize);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/arm_nnexamples_nn_test.cpp

@@ -0,0 +1,801 @@
+/* ----------------------------------------------------------------------
+* Copyright (C) 2010-2018 Arm Limited. All rights reserved.
+*
+*
+* Project:       CMSIS NN Library
+* Title:         arm_nnexamples_nn_test.cpp
+*
+* Description:   Example code for NN kernel testing.
+*
+* Target Processor: Cortex-M cores
+*
+* Redistribution and use in source and binary forms, with or without
+* modification, are permitted provided that the following conditions
+* are met:
+*   - Redistributions of source code must retain the above copyright
+*     notice, this list of conditions and the following disclaimer.
+*   - Redistributions in binary form must reproduce the above copyright
+*     notice, this list of conditions and the following disclaimer in
+*     the documentation and/or other materials provided with the
+*     distribution.
+*   - Neither the name of ARM LIMITED nor the names of its contributors
+*     may be used to endorse or promote products derived from this
+*     software without specific prior written permission.
+*
+* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+* POSSIBILITY OF SUCH DAMAGE.
+* -------------------------------------------------------------------- */
+
+#include "arm_nnexamples_nn_test.h"
+
+//#define TEST_SIGMOID
+//#define TEST_TANH
+#define TEST_POOL
+#define TEST_RELU
+#define TEST_IP
+#define TEST_CONV
+#define TEST_NONSQUARE
+#define TEST_NNMULT
+
+int test_index = 0;
+q7_t test_flags[50];
+bool test_pass;
+
+int main()
+{
+    printf("start tests\n");
+
+    srand(1);
+
+    // common pointers for testing data
+    q7_t     *test1;
+    q15_t    *test2;
+    q7_t     *test3;
+    q15_t    *test4;
+
+    for (test_index = 0; test_index<50; test_index++) {
+        test_flags[test_index] = -1;
+    }
+    test_index = 0;
+
+#ifdef TEST_NNMULT
+#define NNMULT_DIM 128
+    test1 = new q7_t[NNMULT_DIM*2];
+    test2 = new q15_t[NNMULT_DIM*2];
+    test3 = new q7_t[NNMULT_DIM*2];
+    test4 = new q15_t[NNMULT_DIM*2];
+
+    q7_t * mult_out_q7 = test3;
+    q7_t * mult_ref_q7 = test3 + NNMULT_DIM;
+    q15_t * mult_out_q15 = test4;
+    q15_t * mult_ref_q15 = test4 + NNMULT_DIM;
+
+    for (int i=0;i<NNMULT_DIM*2;i++) {
+        test1[i] = (rand() % 256 - 128);
+        test2[i] = (rand() % 65536 - 32768);
+    }
+
+    // Test q7
+    arm_nn_mult_q7(test1, test1+NNMULT_DIM, mult_out_q7, 5, NNMULT_DIM);
+
+    arm_nn_mult_q7_ref(test1, test1+NNMULT_DIM, mult_ref_q7, 5, NNMULT_DIM);
+
+    verify_results_q7(mult_out_q7, mult_ref_q7, NNMULT_DIM);
+
+    arm_nn_mult_q7(test1, test1+NNMULT_DIM, mult_out_q7, 9, NNMULT_DIM);
+
+    arm_nn_mult_q7_ref(test1, test1+NNMULT_DIM, mult_ref_q7, 9, NNMULT_DIM);
+
+    verify_results_q7(mult_out_q7, mult_ref_q7, NNMULT_DIM);
+
+    // Test q15
+    arm_nn_mult_q15(test2, test2+NNMULT_DIM, mult_out_q15, 13, NNMULT_DIM);
+
+    arm_nn_mult_q15_ref(test2, test2+NNMULT_DIM, mult_ref_q15, 13, NNMULT_DIM);
+
+    verify_results_q15(mult_out_q15, mult_ref_q15, NNMULT_DIM);
+
+    arm_nn_mult_q15(test2, test2+NNMULT_DIM, mult_out_q15, 18, NNMULT_DIM);
+
+    arm_nn_mult_q15_ref(test2, test2+NNMULT_DIM, mult_ref_q15, 18, NNMULT_DIM);
+
+    verify_results_q15(mult_out_q15, mult_ref_q15, NNMULT_DIM);
+
+#endif
+
+#ifdef TEST_SIGMOID
+
+#define SIGMOID_DIM 128
+
+    /* This part tests the running of sigmoid functions */
+
+    test1 = new q7_t[SIGMOID_DIM];
+    test2 = new q15_t[SIGMOID_DIM];
+    test3 = new q7_t[SIGMOID_DIM];
+    test4 = new q15_t[SIGMOID_DIM];
+
+    srand(1);
+
+    for (int i = 0; i < SIGMOID_DIM; i++)
+    {
+        test1[i] = (rand() % 256 - 128);
+        test2[i] = (rand() % 65536 - 32768);
+        test3[i] = test1[i];
+        test4[i] = test2[i];
+    }
+
+    arm_nn_activations_direct_q7(test3, SIGMOID_DIM, 3, ARM_SIGMOID);
+
+    for (int i = 0; i < SIGMOID_DIM; i++)
+    {
+        printf("in: %d  out: %d\n", test1[i], test3[i]);
+    }
+
+    printf("start testing q15_t sigmoid\n\n");
+
+    arm_nn_activations_direct_q15(test4, SIGMOID_DIM, 3, ARM_SIGMOID);
+
+    for (int i = 0; i < SIGMOID_DIM; i++)
+    {
+        printf("in: %d  out: %d\n", test2[i], test4[i]);
+    }
+
+    delete[]test1;
+    delete[]test2;
+    delete[]test3;
+    delete[]test4;
+
+#endif
+
+#ifdef TEST_TANH
+
+#define TANH_DIM 128
+
+    /* This part tests the running of sigmoid functions */
+
+    test1 = new q7_t[TANH_DIM];
+    test2 = new q15_t[TANH_DIM];
+    test3 = new q7_t[TANH_DIM];
+    test4 = new q15_t[TANH_DIM];
+
+    srand(1);
+
+    for (int i = 0; i < TANH_DIM; i++)
+    {
+        test1[i] = (rand() % 256 - 128);
+        test2[i] = (rand() % 65536 - 32768);
+        test3[i] = test1[i];
+        test4[i] = test2[i];
+    }
+
+    arm_nn_activations_direct_q7(test3, TANH_DIM, 3, ARM_TANH);
+
+    printf("start testing q7_t tanh\n\n");
+
+    for (int i = 0; i < TANH_DIM; i++)
+    {
+        printf("in: %d  out: %d\n", test1[i], test3[i]);
+    }
+
+    printf("start testing q15_t tanh\n\n");
+
+    arm_nn_activations_direct_q15(test4, TANH_DIM, 3, ARM_TANH);
+
+    for (int i = 0; i < TANH_DIM; i++)
+    {
+        printf("in: %d  out: %d\n", test2[i], test4[i]);
+    }
+
+    delete[]test1;
+    delete[]test2;
+    delete[]test3;
+    delete[]test4;
+
+#endif
+
+#ifdef TEST_POOL
+
+#define POOL_IM_DIM 32
+#define POOL_IM_CH 8
+
+    test1 = new q7_t[POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH * 2];
+    test2 = new q15_t[POOL_IM_DIM * POOL_IM_CH];
+    test3 = new q7_t[POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH];
+
+    for (int i = 0; i < POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH; i++)
+    {
+        test1[i] = (rand() % 256 - 128);
+    }
+
+    q7_t     *img_in = test1 + POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH;
+    q7_t     *pool_out_ref = test3;
+    q7_t     *pool_out_opt = test3 + POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH / 2;
+
+    for (int i = 0; i < POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH; i++)
+    {
+        test3[i] = 0;
+    }
+
+    // copy over the img input
+    for (int i = 0; i < POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH; i++)
+    {
+        img_in[i] = test1[i];
+    }
+
+    initialize_results_q7(pool_out_ref, pool_out_opt, POOL_IM_DIM / 2 * POOL_IM_DIM / 2 * POOL_IM_CH);
+
+    printf("Start maxpool reference implementation\n");
+
+    arm_maxpool_q7_HWC_ref(img_in, POOL_IM_DIM, POOL_IM_CH, 3, 0, 2, POOL_IM_DIM / 2, (q7_t *) test2, pool_out_ref);
+
+    // copy over the img input
+    for (int i = 0; i < POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH; i++)
+    {
+        img_in[i] = test1[i];
+    }
+
+    printf("Start maxpool opt implementation\n");
+
+    arm_maxpool_q7_HWC(img_in, POOL_IM_DIM, POOL_IM_CH, 3, 0, 2, POOL_IM_DIM / 2, (q7_t *) test2, pool_out_opt);
+
+    verify_results_q7(pool_out_ref, pool_out_opt, POOL_IM_DIM / 2 * POOL_IM_DIM / 2 * POOL_IM_CH);
+
+    // copy over the img input
+    for (int i = 0; i < POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH; i++)
+    {
+        img_in[i] = test1[i];
+    }
+
+    // copy over the img input
+    for (int i = 0; i < POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH; i++)
+    {
+        img_in[i] = test1[i];
+    }
+
+    printf("Start avepool ref implementation\n");
+
+    arm_avepool_q7_HWC_ref(img_in, POOL_IM_DIM, POOL_IM_CH, 3, 0, 2, POOL_IM_DIM / 2, (q7_t *) test2, pool_out_ref);
+
+    // copy over the img input
+    for (int i = 0; i < POOL_IM_DIM * POOL_IM_DIM * POOL_IM_CH; i++)
+    {
+        img_in[i] = test1[i];
+    }
+
+    printf("Start avepool opt implementation\n");
+
+    arm_avepool_q7_HWC(img_in, POOL_IM_DIM, POOL_IM_CH, 3, 0, 2, POOL_IM_DIM / 2, (q7_t *) test2, pool_out_opt);
+
+    // special check here
+    bool      if_ave_pool_match = true;
+    for (int i = 0; i < POOL_IM_DIM / 2 * POOL_IM_DIM / 2 * POOL_IM_CH; i++)
+    {
+        // we tolerate at most difference of 1 here because of rounding errors
+        if (pool_out_ref[i] - pool_out_opt[i] >= 2 || pool_out_opt[i] - pool_out_ref[i] >= 2)
+        {
+            printf("Output mismatch at %d, expected %d, actual %d\n", i, pool_out_ref[i], pool_out_opt[i]);
+            if_ave_pool_match = false;
+        }
+    }
+    if (if_ave_pool_match == true)
+    {
+        printf("Outputs match.\n");
+    }
+
+    delete[]test1;
+    delete[]test2;
+    delete[]test3;
+
+#endif
+
+#ifdef TEST_RELU
+
+#define RELU_DIM 127
+
+    test1 = new q7_t[RELU_DIM];
+    test2 = new q15_t[RELU_DIM];
+    test3 = new q7_t[RELU_DIM];
+    test4 = new q15_t[RELU_DIM];
+
+    for (int i = 0; i < RELU_DIM; i++)
+    {
+        test1[i] = (rand() % 256 - 128);
+        test2[i] = (rand() % 65536 - 32768);
+        test3[i] = test1[i];
+        test4[i] = test2[i];
+    }
+
+    q7_t     *relu_ref_data_q7 = test1;
+    q7_t     *relu_opt_data_q7 = test3;
+    q15_t    *relu_ref_data_q15 = test2;
+    q15_t    *relu_opt_data_q15 = test4;
+
+    printf("Start ref relu q7 implementation\n");
+
+    arm_relu_q7_ref(relu_ref_data_q7, RELU_DIM);
+
+    printf("Start opt relu q7 implementation\n");
+
+    arm_relu_q7(relu_opt_data_q7, RELU_DIM);
+
+    verify_results_q7(relu_ref_data_q7, relu_opt_data_q7, RELU_DIM);
+
+    printf("Start ref relu q15 implementation\n");
+
+    arm_relu_q15_ref(relu_ref_data_q15, RELU_DIM);
+
+    printf("Start opt relu q15 implementation\n");
+
+    arm_relu_q15(relu_opt_data_q15, RELU_DIM);
+
+    verify_results_q15(relu_ref_data_q15, relu_opt_data_q15, RELU_DIM);
+
+    delete[]test1;
+    delete[]test2;
+    delete[]test3;
+    delete[]test4;
+
+#endif
+
+#ifdef TEST_IP
+
+#define IP_ROW_DIM 127
+#define IP_COL_DIM 127
+
+    q7_t      ip_weights[IP_ROW_DIM * IP_COL_DIM] = IP2_WEIGHT;
+    q7_t      ip_q7_opt_weights[IP_ROW_DIM * IP_COL_DIM] = IP4_WEIGHT;
+    q7_t      ip_q7_q15_opt_weights[IP_ROW_DIM * IP_COL_DIM] = IP4_q7_q15_WEIGHT;
+    q15_t     ip_q15_weights[IP_ROW_DIM * IP_COL_DIM] = IP2_WEIGHT;
+    q15_t     ip_q15_opt_weights[IP_ROW_DIM * IP_COL_DIM] = IP4_WEIGHT_Q15;
+
+    test1 = new q7_t[IP_COL_DIM + IP_ROW_DIM];
+    test2 = new q15_t[IP_COL_DIM];
+    test3 = new q7_t[IP_ROW_DIM * 3];
+    test4 = new q15_t[IP_COL_DIM + IP_ROW_DIM * 2];
+
+    for (int i = 0; i < IP_ROW_DIM + IP_COL_DIM; i++)
+    {
+        test1[i] = rand() % 256 - 100;
+    }
+    for (int i = 0; i < IP_ROW_DIM * 3; i++)
+    {
+        test3[i] = 0;
+    }
+
+    q7_t     *ip_bias_q7 = test1 + IP_COL_DIM;
+
+    q7_t     *ip_out_q7_ref = test3;
+    q7_t     *ip_out_q7_opt = test3 + IP_ROW_DIM;
+    q7_t     *ip_out_q7_opt_fast = test3 + 2 * IP_ROW_DIM;
+    q15_t    *ip_out_q15_ref = test4 + IP_COL_DIM;
+    q15_t    *ip_out_q15_opt = test4 + IP_COL_DIM + IP_ROW_DIM;
+
+    initialize_results_q7(ip_out_q7_ref, ip_out_q7_opt, IP_ROW_DIM);
+    initialize_results_q7(ip_out_q7_ref, ip_out_q7_opt_fast, IP_ROW_DIM);
+    initialize_results_q7(ip_out_q7_ref, ip_out_q7_opt_fast, IP_ROW_DIM);
+
+    printf("Start ref q7 implementation\n");
+
+    arm_fully_connected_q7_ref(test1, ip_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, ip_bias_q7, ip_out_q7_ref, test2);
+
+    printf("Start q7 implementation\n");
+
+    arm_fully_connected_q7(test1, ip_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, ip_bias_q7, ip_out_q7_opt, test2);
+
+    verify_results_q7(ip_out_q7_ref, ip_out_q7_opt, IP_ROW_DIM);
+
+    printf("Start q7 ref opt implementation\n");
+
+    arm_fully_connected_q7_opt_ref(test1, ip_q7_opt_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, ip_bias_q7,
+                                   ip_out_q7_opt_fast, test2);
+
+    verify_results_q7(ip_out_q7_ref, ip_out_q7_opt_fast, IP_ROW_DIM);
+
+    printf("Start q7 opt implementation\n");
+
+    arm_fully_connected_q7_opt(test1, ip_q7_opt_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, ip_bias_q7, ip_out_q7_opt_fast,
+                               test2);
+
+    verify_results_q7(ip_out_q7_ref, ip_out_q7_opt_fast, IP_ROW_DIM);
+
+    for (int i = 0; i < IP_ROW_DIM + IP_COL_DIM; i++)
+    {
+        test4[i] = (rand() % 65536 - 32768);
+    }
+
+    initialize_results_q15(ip_out_q15_ref, ip_out_q15_opt, IP_ROW_DIM);
+
+    printf("Start ref q15 implementation\n");
+
+    arm_fully_connected_q15_ref(test4, ip_q15_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, test2, ip_out_q15_ref, NULL);
+
+    printf("Start q15 implementation\n");
+
+    arm_fully_connected_q15(test4, ip_q15_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, test2, ip_out_q15_opt, NULL);
+
+    verify_results_q15(ip_out_q15_ref, ip_out_q15_opt, IP_ROW_DIM);
+
+    printf("Start ref opt q15 implementation\n");
+
+    arm_fully_connected_q15_opt_ref(test4, ip_q15_opt_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, test2, ip_out_q15_opt,
+                                    NULL);
+
+    verify_results_q15(ip_out_q15_ref, ip_out_q15_opt, IP_ROW_DIM);
+
+    printf("Start opt q15 implementation\n");
+
+    arm_fully_connected_q15_opt(test4, ip_q15_opt_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, test2, ip_out_q15_opt, NULL);
+
+    verify_results_q15(ip_out_q15_ref, ip_out_q15_opt, IP_ROW_DIM);
+
+    initialize_results_q15(ip_out_q15_ref, ip_out_q15_opt, IP_ROW_DIM);
+
+    printf("Start ref q7_q15 implementation\n");
+
+    arm_fully_connected_mat_q7_vec_q15_ref(test4, ip_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, ip_bias_q7, ip_out_q15_ref,
+                                           test2);
+
+    printf("Start q7_q15 implementation\n");
+
+    arm_fully_connected_mat_q7_vec_q15(test4, ip_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, ip_bias_q7, ip_out_q15_opt,
+                                       test2);
+
+    verify_results_q15(ip_out_q15_ref, ip_out_q15_opt, IP_ROW_DIM);
+
+    printf("Start ref opt q7_q15 implementation\n");
+
+    arm_fully_connected_mat_q7_vec_q15_opt_ref(test4, ip_q7_q15_opt_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, ip_bias_q7,
+                                               ip_out_q15_opt, test2);
+
+    verify_results_q15(ip_out_q15_ref, ip_out_q15_opt, IP_ROW_DIM);
+
+    printf("Start opt q7_q15 implementation\n");
+
+    arm_fully_connected_mat_q7_vec_q15_opt(test4, ip_q7_q15_opt_weights, IP_COL_DIM, IP_ROW_DIM, 1, 7, ip_bias_q7,
+                                           ip_out_q15_opt, test2);
+
+    verify_results_q15(ip_out_q15_ref, ip_out_q15_opt, IP_ROW_DIM);
+
+    delete[]test1;
+    delete[]test2;
+    delete[]test3;
+    delete[]test4;
+
+#endif
+
+#ifdef TEST_NONSQUARE
+
+/* Use RCONV to differential with square CONV */
+
+#define RCONV_IM_DIM_X 10
+#define RCONV_IM_DIM_Y 8
+#define RCONV_IM_CH 4
+#define RCONV_KER_DIM_X 5
+#define RCONV_KER_DIM_Y 3
+#define RCONV_STRIDE_X 1
+#define RCONV_STRIDE_Y 1
+#define RCONV_PADDING_X 2
+#define RCONV_PADDING_Y 1
+#define RCONV_OUT_CH 4
+#define RCONV_OUT_DIM_X 10
+#define RCONV_OUT_DIM_Y 8
+
+    test1 = new q7_t[RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH * RCONV_OUT_CH + RCONV_OUT_CH];
+    test2 = new q15_t[2 * RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH];
+    test3 =
+        new q7_t[RCONV_IM_DIM_Y * RCONV_IM_DIM_X * RCONV_IM_CH + 2 * RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH];
+
+    for (int i = 0; i < RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH * RCONV_OUT_CH + RCONV_OUT_CH; i++)
+    {
+        test1[i] = rand() % 256 - 100;
+    }
+
+    for (int i = 0;
+         i < RCONV_IM_DIM_Y * RCONV_IM_DIM_X * RCONV_IM_CH + 2 * RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH; i++)
+    {
+        test3[i] = rand() % 256 - 100;
+    }
+
+    q7_t     *rconv_weight_q7 = test1;
+    q7_t     *rconv_bias_q7 = test1 + RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH * RCONV_OUT_CH;
+
+    q15_t    *rconv_buf = test2;
+
+    q7_t     *rconv_im_in_q7 = test3;
+    q7_t     *rconv_im_out_ref_q7 = test3 + RCONV_IM_DIM_Y * RCONV_IM_DIM_X * RCONV_IM_CH;
+    q7_t     *rconv_im_out_opt_q7 =
+        test3 + RCONV_IM_DIM_Y * RCONV_IM_DIM_X * RCONV_IM_CH + RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH;
+
+    initialize_results_q7(rconv_im_out_ref_q7, rconv_im_out_opt_q7, RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH);
+
+    printf("start conv q7 nonsquare ref implementation\n");
+    arm_convolve_HWC_q7_ref_nonsquare(rconv_im_in_q7, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH, rconv_weight_q7,
+                                      RCONV_OUT_CH, RCONV_KER_DIM_X, RCONV_KER_DIM_Y, RCONV_PADDING_X, RCONV_PADDING_Y,
+                                      RCONV_STRIDE_X, RCONV_STRIDE_Y, rconv_bias_q7, 1, 7, rconv_im_out_ref_q7,
+                                      RCONV_OUT_DIM_X, RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+    printf("start conv q7 nonsquare opt implementation\n");
+    arm_convolve_HWC_q7_fast_nonsquare(rconv_im_in_q7, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH, rconv_weight_q7,
+                                       RCONV_OUT_CH, RCONV_KER_DIM_X, RCONV_KER_DIM_Y, RCONV_PADDING_X, RCONV_PADDING_Y,
+                                       RCONV_STRIDE_X, RCONV_STRIDE_Y, rconv_bias_q7, 1, 7, rconv_im_out_opt_q7,
+                                       RCONV_OUT_DIM_X, RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+    verify_results_q7(rconv_im_out_ref_q7, rconv_im_out_opt_q7, RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH);
+
+    initialize_results_q7(rconv_im_out_ref_q7, rconv_im_out_opt_q7, RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH);
+
+    printf("start conv q7 nonsquare ref implementation\n");
+    arm_convolve_HWC_q7_ref_nonsquare(rconv_im_in_q7, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH, rconv_weight_q7,
+                                      RCONV_OUT_CH, RCONV_KER_DIM_X, RCONV_KER_DIM_Y, RCONV_PADDING_X, RCONV_PADDING_Y,
+                                      RCONV_STRIDE_X, RCONV_STRIDE_Y, rconv_bias_q7, 1, 7, rconv_im_out_ref_q7,
+                                      RCONV_OUT_DIM_X, RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+    printf("start conv q7 nonsquare basic implementation\n");
+    arm_convolve_HWC_q7_basic_nonsquare(rconv_im_in_q7, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH, rconv_weight_q7,
+                                       RCONV_OUT_CH, RCONV_KER_DIM_X, RCONV_KER_DIM_Y, RCONV_PADDING_X, RCONV_PADDING_Y,
+                                       RCONV_STRIDE_X, RCONV_STRIDE_Y, rconv_bias_q7, 1, 7, rconv_im_out_opt_q7,
+                                       RCONV_OUT_DIM_X, RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+    verify_results_q7(rconv_im_out_ref_q7, rconv_im_out_opt_q7, RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH);
+
+    initialize_results_q7(rconv_im_out_ref_q7, rconv_im_out_opt_q7, RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH);
+
+    printf("start 1x1 conv q7 nonsquare fast implementation\n");
+    arm_convolve_HWC_q7_fast_nonsquare(rconv_im_in_q7, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH, rconv_weight_q7,
+                                       RCONV_OUT_CH, 1, 1, 0, 0, RCONV_STRIDE_X,
+                                       RCONV_STRIDE_Y, rconv_bias_q7, 1, 7, rconv_im_out_ref_q7, RCONV_OUT_DIM_X,
+                                       RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+    printf("start 1x1 conv q7 nonsquare dedicated function implementation\n");
+    arm_convolve_1x1_HWC_q7_fast_nonsquare(rconv_im_in_q7, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH, rconv_weight_q7,
+                                           RCONV_OUT_CH, 1, 1, 0, 0, RCONV_STRIDE_X,
+                                           RCONV_STRIDE_Y, rconv_bias_q7, 1, 7, rconv_im_out_opt_q7, RCONV_OUT_DIM_X,
+                                           RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+    verify_results_q7(rconv_im_out_ref_q7, rconv_im_out_opt_q7, RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH);
+
+    printf("start depthwise separable conv q7 nonsquare ref implementation\n");
+    arm_depthwise_separable_conv_HWC_q7_ref_nonsquare(rconv_im_in_q7, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH,
+                                                      rconv_weight_q7, RCONV_OUT_CH, RCONV_KER_DIM_X, RCONV_KER_DIM_Y,
+                                                      RCONV_PADDING_X, RCONV_PADDING_Y, RCONV_STRIDE_X, RCONV_STRIDE_Y,
+                                                      rconv_bias_q7, 1, 7, rconv_im_out_ref_q7, RCONV_OUT_DIM_X,
+                                                      RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+    printf("start depthwise separable conv q7 nonsquare opt implementation\n");
+    arm_depthwise_separable_conv_HWC_q7_nonsquare(rconv_im_in_q7, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH,
+                                                  rconv_weight_q7, RCONV_OUT_CH, RCONV_KER_DIM_X, RCONV_KER_DIM_Y,
+                                                  RCONV_PADDING_X, RCONV_PADDING_Y, RCONV_STRIDE_X, RCONV_STRIDE_Y,
+                                                  rconv_bias_q7, 1, 7, rconv_im_out_opt_q7, RCONV_OUT_DIM_X,
+                                                  RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+    verify_results_q7(rconv_im_out_ref_q7, rconv_im_out_opt_q7, RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH);
+
+    delete[]test1;
+    delete[]test2;
+    delete[]test3;
+	
+	test2 = new q15_t[RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH * RCONV_OUT_CH + RCONV_OUT_CH]; // weights + bias
+	test4 = new q15_t[2 * RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH   //buffer
+	         + RCONV_IM_DIM_Y * RCONV_IM_DIM_X * RCONV_IM_CH + 2 * RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH]; // i/o
+
+    for (int i = 0; i < RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH * RCONV_OUT_CH + RCONV_OUT_CH; i++)
+    {
+        test2[i] = rand() % 256 - 100;
+    }
+
+    for (int i = 0;
+         i < 2 * RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH
+         + RCONV_IM_DIM_Y * RCONV_IM_DIM_X * RCONV_IM_CH + 2 * RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH;
+        i++)
+    {
+        test4[i] = rand() % 256 - 100;
+    }
+
+    q15_t     *rconv_weight_q15 = test2;
+    q15_t     *rconv_bias_q15 = test2 + RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH * RCONV_OUT_CH;
+
+    rconv_buf = test4;
+
+    q15_t     *rconv_im_in_q15 = test4 + 2 * RCONV_KER_DIM_Y * RCONV_KER_DIM_X * RCONV_IM_CH;
+    q15_t     *rconv_im_out_ref_q15 = rconv_im_in_q15 + RCONV_IM_DIM_Y * RCONV_IM_DIM_X * RCONV_IM_CH;
+    q15_t     *rconv_im_out_opt_q15 = rconv_im_out_ref_q15 + RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH;
+
+    initialize_results_q15(rconv_im_out_ref_q15, rconv_im_out_opt_q15, RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH);
+
+    printf("start conv q15 nonsquare ref implementation\n");
+    arm_convolve_HWC_q15_nonsquare_ref(rconv_im_in_q15, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH, rconv_weight_q15,
+                                      RCONV_OUT_CH, RCONV_KER_DIM_X, RCONV_KER_DIM_Y, RCONV_PADDING_X, RCONV_PADDING_Y,
+                                      RCONV_STRIDE_X, RCONV_STRIDE_Y, rconv_bias_q15, 1, 7, rconv_im_out_ref_q15,
+                                      RCONV_OUT_DIM_X, RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+    printf("start conv q5 nonsquare opt implementation\n");
+    arm_convolve_HWC_q15_fast_nonsquare(rconv_im_in_q15, RCONV_IM_DIM_X, RCONV_IM_DIM_Y, RCONV_IM_CH, rconv_weight_q15,
+                                       RCONV_OUT_CH, RCONV_KER_DIM_X, RCONV_KER_DIM_Y, RCONV_PADDING_X, RCONV_PADDING_Y,
+                                       RCONV_STRIDE_X, RCONV_STRIDE_Y, rconv_bias_q15, 1, 7, rconv_im_out_opt_q15,
+                                       RCONV_OUT_DIM_X, RCONV_OUT_DIM_Y, rconv_buf, NULL);
+
+    verify_results_q15(rconv_im_out_ref_q15, rconv_im_out_opt_q15, RCONV_OUT_DIM_Y * RCONV_OUT_DIM_X * RCONV_OUT_CH);
+	
+    delete [] test2;
+    delete [] test4;
+#endif
+
+#ifdef TEST_CONV
+
+#define CONV_IM_DIM 16
+#define CONV_IM_CH 16
+#define CONV_KER_DIM 5
+#define CONV_OUT_CH 16
+#define CONV_OUT_DIM 16
+
+    test1 = new q7_t[CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH + CONV_OUT_CH];
+    test2 =
+        new q15_t[CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH +
+                  2 * CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH + CONV_OUT_CH];
+    test3 = new q7_t[CONV_IM_DIM * CONV_IM_DIM * CONV_IM_CH + 2 * CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH];
+    test4 = new q15_t[CONV_IM_DIM * CONV_IM_DIM * CONV_IM_CH + 2 * CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH];
+
+    for (int i = 0; i < CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH + CONV_OUT_CH; i++)
+    {
+        test1[i] = rand() % 256 - 100;
+    }
+
+    for (int i = 0;
+         i <
+         CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH +
+         2 * CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH + CONV_OUT_CH; i++)
+    {
+        test2[i] = (rand() % 65536 - 32768);
+    }
+
+    for (int i = 0; i < CONV_IM_DIM * CONV_IM_DIM * CONV_IM_CH + 2 * CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH; i++)
+    {
+        test3[i] = rand() % 256 - 100;
+    }
+
+    for (int i = 0; i < CONV_IM_DIM * CONV_IM_DIM * CONV_IM_CH + 2 * CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH; i++)
+    {
+        test4[i] = (rand() % 65536 - 32768);
+    }
+
+    q7_t     *conv_weight_q7 = test1;
+    q7_t     *conv_bias_q7 = test1 + CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH;
+
+    q15_t    *conv_weight_q15 = test2;
+    q15_t    *conv_buf = test2 + CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH;
+    q15_t    *conv_bias_q15 =
+        test2 + CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH +
+        2 * CONV_KER_DIM * CONV_KER_DIM * CONV_IM_CH * CONV_OUT_CH;
+
+    q7_t     *conv_im_in_q7 = test3;
+    q7_t     *conv_im_out_ref_q7 = test3 + CONV_IM_DIM * CONV_IM_DIM * CONV_IM_CH;
+    q7_t     *conv_im_out_opt_q7 =
+        test3 + CONV_IM_DIM * CONV_IM_DIM * CONV_IM_CH + CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH;
+
+    q15_t    *conv_im_in_q15 = test4;
+    q15_t    *conv_im_out_ref_q15 = test4 + CONV_IM_DIM * CONV_IM_DIM * CONV_IM_CH;
+    q15_t    *conv_im_out_opt_q15 =
+        test4 + CONV_IM_DIM * CONV_IM_DIM * CONV_IM_CH + CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH;
+
+    initialize_results_q7(conv_im_out_ref_q7, conv_im_out_opt_q7, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+    printf("start q7 ref implementation\n");
+
+    arm_convolve_HWC_q7_ref(conv_im_in_q7, CONV_IM_DIM, CONV_IM_CH, conv_weight_q7,
+                            CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q7, 1, 7, conv_im_out_ref_q7,
+                            CONV_OUT_DIM, conv_buf, NULL);
+
+    printf("start q7 basic implementation\n");
+
+    arm_convolve_HWC_q7_basic(conv_im_in_q7, CONV_IM_DIM, CONV_IM_CH, conv_weight_q7,
+                              CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q7, 1, 7, conv_im_out_opt_q7,
+                              CONV_OUT_DIM, conv_buf, NULL);
+
+    verify_results_q7(conv_im_out_ref_q7, conv_im_out_opt_q7, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+    printf("start q7 fast implementation\n");
+
+    arm_convolve_HWC_q7_fast(conv_im_in_q7, CONV_IM_DIM, CONV_IM_CH, conv_weight_q7,
+                             CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q7, 1, 7, conv_im_out_opt_q7,
+                             CONV_OUT_DIM, conv_buf, NULL);
+
+    verify_results_q7(conv_im_out_ref_q7, conv_im_out_opt_q7, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+    // testing with RGB
+    printf("start q7 ref implementation for RGB\n");
+
+    arm_convolve_HWC_q7_ref(conv_im_in_q7, CONV_IM_DIM, 3, conv_weight_q7,
+                            CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q7, 1, 7, conv_im_out_ref_q7,
+                            CONV_OUT_DIM, conv_buf, NULL);
+
+    printf("start q7 basic implementation for RGB\n");
+
+    arm_convolve_HWC_q7_basic(conv_im_in_q7, CONV_IM_DIM, 3, conv_weight_q7,
+                              CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q7, 1, 7, conv_im_out_opt_q7,
+                              CONV_OUT_DIM, conv_buf, NULL);
+
+    verify_results_q7(conv_im_out_ref_q7, conv_im_out_opt_q7, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+    printf("start q7 RGB implementation for RGB\n");
+
+    arm_convolve_HWC_q7_RGB(conv_im_in_q7, CONV_IM_DIM, 3, conv_weight_q7,
+                            CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q7, 1, 7, conv_im_out_opt_q7,
+                            CONV_OUT_DIM, conv_buf, NULL);
+
+    verify_results_q7(conv_im_out_ref_q7, conv_im_out_opt_q7, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+    // testing q15
+    initialize_results_q15(conv_im_out_ref_q15, conv_im_out_opt_q15, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+    printf("start q15 ref implementation\n");
+
+    arm_convolve_HWC_q15_ref(conv_im_in_q15, CONV_IM_DIM, CONV_IM_CH, conv_weight_q15,
+                             CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q15, 0, 15, conv_im_out_ref_q15,
+                             CONV_OUT_DIM, conv_buf, NULL);
+
+    printf("start q15 basic implementation\n");
+
+    arm_convolve_HWC_q15_basic(conv_im_in_q15, CONV_IM_DIM, CONV_IM_CH, conv_weight_q15,
+                               CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q15, 0, 15, conv_im_out_opt_q15,
+                               CONV_OUT_DIM, conv_buf, NULL);
+
+    verify_results_q15(conv_im_out_ref_q15, conv_im_out_opt_q15, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+    printf("start q15 fast implementation\n");
+
+    arm_convolve_HWC_q15_fast(conv_im_in_q15, CONV_IM_DIM, CONV_IM_CH, conv_weight_q15,
+                              CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q15, 0, 15, conv_im_out_opt_q15,
+                              CONV_OUT_DIM, conv_buf, NULL);
+
+    verify_results_q15(conv_im_out_ref_q15, conv_im_out_opt_q15, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+    // depthwise separable conv
+    initialize_results_q7(conv_im_out_ref_q7, conv_im_out_opt_q7, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+    printf("start q7 depthwise_separable_conv ref implementation\n");
+
+    arm_depthwise_separable_conv_HWC_q7_ref(conv_im_in_q7, CONV_IM_DIM, CONV_IM_CH, conv_weight_q7,
+                                            CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q7, 1, 7, conv_im_out_ref_q7,
+                                            CONV_OUT_DIM, conv_buf, NULL);
+
+    printf("start q7 depthwise_separable_conv implementation\n");
+
+    arm_depthwise_separable_conv_HWC_q7(conv_im_in_q7, CONV_IM_DIM, CONV_IM_CH, conv_weight_q7,
+                                        CONV_OUT_CH, CONV_KER_DIM, 2, 1, conv_bias_q7, 1, 7, conv_im_out_opt_q7,
+                                        CONV_OUT_DIM, conv_buf, NULL);
+
+    verify_results_q7(conv_im_out_ref_q7, conv_im_out_opt_q7, CONV_OUT_DIM * CONV_OUT_DIM * CONV_OUT_CH);
+
+    delete[]test1;
+    delete[]test2;
+    delete[]test3;
+    delete[]test4;
+
+#endif
+
+    test_pass = true;
+    test_index = 0;
+    while (test_flags[test_index] != -1) {
+        if (test_flags[test_index]) {
+             test_pass = false;
+        }
+        test_index ++;
+    }
+    if (test_pass) {
+        printf("All tests passed\n");
+    } else {
+        printf("Test failed passed\n");
+    }
+
+    return 0;
+}

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/arm_nnexamples_nn_test.h

@@ -0,0 +1,78 @@
+#ifndef _MAIN_H_
+#define _MAIN_H_
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+
+#include "arm_math.h"
+
+#include "arm_nnfunctions.h"
+#include "ref_functions.h"
+
+extern int test_index;
+extern q7_t test_flags[50];
+
+void initialize_results_q7(q7_t * ref, q7_t * opt, int length)
+{
+    arm_fill_q7(0, ref, length);
+    arm_fill_q7(37, opt, length);
+}
+
+void initialize_results_q15(q15_t * ref, q15_t * opt, int length)
+{
+    arm_fill_q15(0, ref, length);
+    arm_fill_q15(0x5F5, opt, length);
+}
+
+void verify_results_q7(q7_t * ref, q7_t * opt, int length)
+{
+
+    bool      if_match = true;
+
+    for (int i = 0; i < length; i++)
+    {
+        if (ref[i] != opt[i])
+        {
+            printf("Output mismatch at %d, expected %d, actual %d\r\n", i, ref[i], opt[i]);
+
+            if_match = false;
+        }
+    }
+
+    if (if_match == true)
+    {
+        printf("Outputs match.\r\n\r\n");
+        test_flags[test_index++] = 0;
+    } else {
+        test_flags[test_index++] = 1;
+    }
+
+}
+
+void verify_results_q15(q15_t * ref, q15_t * opt, int length)
+{
+
+    bool      if_match = true;
+
+    for (int i = 0; i < length; i++)
+    {
+        if (ref[i] != opt[i])
+        {
+            printf("Output mismatch at %d, expected %d, actual %d\r\n", i, ref[i], opt[i]);
+
+            if_match = false;
+        }
+    }
+
+    if (if_match == true)
+    {
+        printf("Outputs match.\r\n\r\n");
+        test_flags[test_index++] = 0;
+    } else {
+        test_flags[test_index++] = 1;
+    }
+
+}
+
+#endif

Drivers/CMSIS/NN/NN_Lib_Tests/nn_test/readme.txt

@@ -0,0 +1,4 @@
+CMSIS DSP_Lib example arm_nnexample_nn_test for
+  Cortex-M3, Cortex-M4 and Cortex-M7.
+
+The example is configured for uVision Simulator.

Drivers/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q15.c

@@ -0,0 +1,101 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nn_activations_q15.c
+ * Description:  Q15 neural network activation function using direct table look-up
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_common_tables.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Acti
+ * @{
+ */
+
+  /**
+   * @brief Q15 neural network activation function using direct table look-up
+   * @param[in,out]   data        pointer to input
+   * @param[in]       size        number of elements
+   * @param[in]       int_width   bit-width of the integer part, assume to be smaller than 3
+   * @param[in]       type        type of activation functions
+   * @return none.
+   *
+   * @details
+   * 
+   * This is the direct table look-up approach.
+   *
+   * Assume here the integer part of the fixed-point is <= 3.
+   * More than 3 just not making much sense, makes no difference with
+   * saturation followed by any of these activation functions. 
+   */
+
+void arm_nn_activations_direct_q15(q15_t * data, uint16_t size, uint16_t int_width, arm_nn_activation_type type)
+{
+    uint16_t  i = size;
+    q15_t    *pIn = data;
+    q15_t    *pOut = data;
+    uint16_t  shift_size = 8 + 3 - int_width;
+    uint32_t  bit_mask = 0x7FF >> int_width;
+    uint32_t  full_frac = bit_mask + 1;
+    const q15_t *lookup_table;
+
+    switch (type)
+    {
+    case ARM_SIGMOID:
+        lookup_table = sigmoidTable_q15;
+        break;
+    case ARM_TANH:
+    default:
+        lookup_table = tanhTable_q15;
+        break;
+    }
+
+    while (i)
+    {
+        q15_t     out;
+        q15_t     in = *pIn++;
+        q15_t     frac = (uint32_t) in & bit_mask;
+        q15_t     value = lookup_table[__USAT(in >> shift_size, 8)];
+        q15_t     value2 = lookup_table[__USAT(1 + (in >> shift_size), 8)];
+
+        /* doing the interpolation here for better accuracy */
+        out = ((q31_t) (full_frac - frac) * value + (q31_t) value2 * frac) >> shift_size;
+
+        *pOut++ = out;
+        i--;
+    }
+
+}
+
+/**
+ * @} end of Acti group
+ */

Drivers/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q7.c

@@ -0,0 +1,91 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nn_activations_q7.c
+ * Description:  Q7 neural network activation function using direct table look-up
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_common_tables.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Acti
+ * @{
+ */
+
+  /**
+   * @brief Q7 neural network activation function using direct table look-up
+   * @param[in,out]   data        pointer to input
+   * @param[in]       size        number of elements
+   * @param[in]       int_width   bit-width of the integer part, assume to be smaller than 3
+   * @param[in]       type        type of activation functions
+   * @return none.
+   *
+   * @details
+   * 
+   * This is the direct table look-up approach.
+   *
+   * Assume here the integer part of the fixed-point is <= 3.
+   * More than 3 just not making much sense, makes no difference with
+   * saturation followed by any of these activation functions. 
+   */
+
+void arm_nn_activations_direct_q7(q7_t * data, uint16_t size, uint16_t int_width, arm_nn_activation_type type)
+{
+    uint16_t  i = size;
+    q7_t     *pIn = data;
+    q7_t     *pOut = data;
+    q7_t      in;
+    q7_t      out;
+    uint16_t  shift_size = 3 - int_width;
+    const q7_t *lookup_table;
+    switch (type)
+    {
+    case ARM_SIGMOID:
+        lookup_table = sigmoidTable_q7;
+        break;
+    case ARM_TANH:
+    default:
+        lookup_table = tanhTable_q7;
+        break;
+    }
+    while (i)
+    {
+        in = *pIn++;
+        out = lookup_table[(uint8_t) (in >> shift_size)];
+        *pOut++ = out;
+        i--;
+    }
+}
+
+/**
+ * @} end of Acti group
+ */

Drivers/CMSIS/NN/Source/ActivationFunctions/arm_relu_q15.c

@@ -0,0 +1,106 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_relu_q15.c
+ * Description:  Q15 version of ReLU
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Acti
+ * @{
+ */
+
+  /**
+   * @brief Q15 RELU function
+   * @param[in,out]   data        pointer to input
+   * @param[in]       size        number of elements
+   * @return none.
+   * 
+   * @details
+   *
+   * Optimized relu with QSUB instructions.
+   *
+   */
+
+void arm_relu_q15(q15_t * data, uint16_t size)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    uint16_t  i = size >> 1;
+    q15_t    *pIn = data;
+    q15_t    *pOut = data;
+    q31_t     in;
+    q31_t     buf;
+    q31_t     mask;
+
+    while (i)
+    {
+        in = *__SIMD32(pIn)++;
+
+        /* extract the first bit */
+        buf = __ROR(in & 0x80008000, 15);
+
+        /* if MSB=1, mask will be 0xFF, 0x0 otherwise */
+        mask = __QSUB16(0x00000000, buf);
+
+        *__SIMD32(pOut)++ = in & (~mask);
+        i--;
+    }
+
+    if (size & 0x1)
+    {
+        if (*pIn < 0)
+        {
+            *pIn = 0;
+        }
+        pIn++;
+    }
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    uint16_t  i;
+
+    for (i = 0; i < size; i++)
+    {
+        if (data[i] < 0)
+            data[i] = 0;
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+}
+
+/**
+ * @} end of Acti group
+ */

Drivers/CMSIS/NN/Source/ActivationFunctions/arm_relu_q7.c

@@ -0,0 +1,110 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_relu_q7.c
+ * Description:  Q7 version of ReLU
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Acti
+ * @{
+ */
+
+  /**
+   * @brief Q7 RELU function
+   * @param[in,out]   data        pointer to input
+   * @param[in]       size        number of elements
+   * @return none.
+   * 
+   * @details
+   *
+   * Optimized relu with QSUB instructions.
+   *
+   */
+
+void arm_relu_q7(q7_t * data, uint16_t size)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    uint16_t  i = size >> 2;
+    q7_t     *pIn = data;
+    q7_t     *pOut = data;
+    q31_t     in;
+    q31_t     buf;
+    q31_t     mask;
+
+    while (i)
+    {
+        in = *__SIMD32(pIn)++;
+
+        /* extract the first bit */
+        buf = __ROR(in & 0x80808080, 7);
+
+        /* if MSB=1, mask will be 0xFF, 0x0 otherwise */
+        mask = __QSUB8(0x00000000, buf);
+
+        *__SIMD32(pOut)++ = in & (~mask);
+        i--;
+    }
+
+    i = size & 0x3;
+    while (i)
+    {
+        if (*pIn < 0)
+        {
+            *pIn = 0;
+        }
+        pIn++;
+        i--;
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+    uint16_t  i;
+
+    for (i = 0; i < size; i++)
+    {
+        if (data[i] < 0)
+            data[i] = 0;
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+}
+
+/**
+ * @} end of Acti group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c

@@ -0,0 +1,235 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_convolve_1x1_HWC_q7_fast_nonsquare.c
+ * Description:  Fast Q7 version of 1x1 convolution (non-square shape)
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+/**
+ * @brief Fast Q7 version of 1x1 convolution (non-sqaure shape)
+ * @param[in]       Im_in        pointer to input tensor
+ * @param[in]       dim_im_in_x  input tensor dimention x
+ * @param[in]       dim_im_in_y  input tensor dimention y
+ * @param[in]       ch_im_in     number of input tensor channels
+ * @param[in]       wt           pointer to kernel weights
+ * @param[in]       ch_im_out    number of filters, i.e., output tensor channels
+ * @param[in]       dim_kernel_x filter kernel size x
+ * @param[in]       dim_kernel_y filter kernel size y
+ * @param[in]       padding_x    padding size x
+ * @param[in]       padding_y    padding size y
+ * @param[in]       stride_x     convolution stride x
+ * @param[in]       stride_y     convolution stride y
+ * @param[in]       bias         pointer to bias
+ * @param[in]       bias_shift   amount of left-shift for bias
+ * @param[in]       out_shift    amount of right-shift for output
+ * @param[in,out]   Im_out       pointer to output tensor
+ * @param[in]       dim_im_out_x output tensor dimension x
+ * @param[in]       dim_im_out_y output tensor dimension y
+ * @param[in,out]   bufferA      pointer to buffer space for input 
+ * @param[in,out]   bufferB      pointer to buffer space for output
+ * @return     The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is optimized for convolution with 1x1 kernel size (i.e., dim_kernel_x=1
+ * and dim_kernel_y=1). It can be used for the second half of MobileNets [1] after depthwise 
+ * separable convolution.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ *   ch_im_in is multiple of 4
+ *   ch_im_out is multiple of 2
+ *
+ * [1] MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
+ * https://arxiv.org/abs/1704.04861
+ */
+
+arm_status arm_convolve_1x1_HWC_q7_fast_nonsquare(const q7_t * Im_in,
+                                                  const uint16_t dim_im_in_x,
+                                                  const uint16_t dim_im_in_y,
+                                                  const uint16_t ch_im_in,
+                                                  const q7_t * wt,
+                                                  const uint16_t ch_im_out,
+                                                  const uint16_t dim_kernel_x,
+                                                  const uint16_t dim_kernel_y,
+                                                  const uint16_t padding_x,
+                                                  const uint16_t padding_y,
+                                                  const uint16_t stride_x,
+                                                  const uint16_t stride_y,
+                                                  const q7_t * bias,
+                                                  const uint16_t bias_shift,
+                                                  const uint16_t out_shift,
+                                                  q7_t * Im_out,
+                                                  const uint16_t dim_im_out_x,
+                                                  const uint16_t dim_im_out_y, 
+                                                  q15_t * bufferA, 
+                                                  q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    int16_t   i_out_y, i_out_x;
+    int16_t   i_ch_out;
+
+    /* -----------------------
+     *  Here we use bufferA as q15_t internally as computation are done with q15_t level
+     *  im2col are done to output in q15_t format from q7_t input
+     */
+
+    q15_t    *pBuffer = bufferA;
+    q7_t     *pOut = Im_out;
+
+    if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0 || dim_kernel_x != 1 || dim_kernel_y != 1
+        || padding_x != 0 || padding_y != 0 || stride_x != 1 || stride_y != 1)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_out_y * dim_im_in_x + i_out_x) * ch_im_in, pBuffer,
+                                             ch_im_in);
+            pBuffer += ch_im_in;
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in, bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    /* check if there is left-over for compute */
+    if (pBuffer != bufferA)
+    {
+        const q7_t *pA = wt;
+        for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++)
+        {
+            q31_t     sum = ((q31_t)(bias[i_ch_out]) << bias_shift) + NN_ROUND(out_shift);
+            q15_t    *pB = bufferA;
+            /* basically each time it process 4 entries */
+            uint16_t  colCnt = ch_im_in * dim_kernel_x * dim_kernel_y >> 2;
+
+            while (colCnt)
+            {
+
+                q31_t     inA1, inA2;
+                q31_t     inB1, inB2;
+
+                pA = (const q7_t *)read_and_pad_reordered((void *)pA, &inA1, &inA2);
+
+                inB1 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA1, inB1, sum);
+                inB2 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA2, inB2, sum);
+
+                colCnt--;
+            }
+            colCnt = ch_im_in * dim_kernel_y * dim_kernel_x & 0x3;
+            while (colCnt)
+            {
+                q7_t      inA1 = *pA++;
+                q15_t     inB1 = *pB++;
+                sum += inA1 * inB1;
+                colCnt--;
+            }
+            *pOut = (q7_t) __SSAT((sum >> out_shift), 8);
+            pOut++;
+
+        }
+
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+		
+    int       i, j, k, l, m, n;
+    int       conv_out;
+    int       in_row, in_col;
+
+    if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0 || dim_kernel_x != 1 || dim_kernel_y != 1
+        || padding_x != 0 || padding_y != 0 || stride_x != 1 || stride_y != 1)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out_y; j++)
+        {
+            for (k = 0; k < dim_im_out_x; k++)
+            {
+                conv_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+                for (m = 0; m < dim_kernel_y; m++)
+                {
+                    for (n = 0; n < dim_kernel_x; n++)
+                    {
+                        // if-for implementation
+                        in_row = stride_y * j + m - padding_y;
+                        in_col = stride_x * k + n - padding_x;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] *
+                                    wt[i * ch_im_in * dim_kernel_y * dim_kernel_x + (m * dim_kernel_y + n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_basic.c

@@ -0,0 +1,207 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_convolve_HWC_q15_basic.c
+ * Description:  Q15 version of convolution
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+  /**
+   * @brief Basic Q15 convolution function
+   * @param[in]       Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       wt          pointer to kernel weights
+   * @param[in]       ch_im_out   number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       bias        pointer to bias
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input
+   * @param[in,out]   bufferB     pointer to buffer space for output
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code> 
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * bufferA size: ch_im_in*dim_kernel*dim_kernel
+   *
+   * bufferB size: 0
+   *
+   * This basic version is designed to work for any input tensor and weight
+   * dimension. 
+   */
+
+arm_status
+arm_convolve_HWC_q15_basic(const q15_t * Im_in,
+                           const uint16_t dim_im_in,
+                           const uint16_t ch_im_in,
+                           const q15_t * wt,
+                           const uint16_t ch_im_out,
+                           const uint16_t dim_kernel,
+                           const uint16_t padding,
+                           const uint16_t stride,
+                           const q15_t * bias,
+                           const uint16_t bias_shift,
+                           const uint16_t out_shift,
+                           q15_t * Im_out, 
+                           const uint16_t dim_im_out, 
+                           q15_t * bufferA, 
+                           q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    int16_t   i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+    uint16_t  im2col_out_pixel_index = 0;
+    q15_t    *pBuffer = bufferA;
+    q15_t    *pOut = Im_out;
+    q15_t    *im_buffer = bufferA;
+    const q15_t *pA;
+    int       i;
+
+    /* This part implements the im2col function */
+    for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+        {
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+                    {
+                        /* Filling 0 for out-of-bound paddings */
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        /* arm_copy_q15((q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
+                        memcpy(pBuffer, (q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, sizeof(q15_t)*ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            pA = wt;
+            for (i = 0; i < ch_im_out; i++)
+            {
+                q31_t     sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                q15_t    *pB = im_buffer;
+                uint16_t  colCnt = ch_im_in * dim_kernel * dim_kernel >> 2;
+                while (colCnt)
+                {
+                    q31_t     inA1 = *__SIMD32(pA)++;
+                    q31_t     inB1 = *__SIMD32(pB)++;
+                    q31_t     inA2 = *__SIMD32(pA)++;
+                    q31_t     inB2 = *__SIMD32(pB)++;
+
+                    sum = __SMLAD(inA1, inB1, sum);
+                    sum = __SMLAD(inA2, inB2, sum);
+
+                    colCnt--;
+                }
+                colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3;
+                while (colCnt)
+                {
+                    q15_t     inA1 = *pA++;
+                    q15_t     inB1 = *pB++;
+                    sum += inA1 * inB1;
+                    colCnt--;
+                }
+                *pOut = (q15_t) __SSAT((sum >> out_shift), 16);
+                pOut++;
+            }
+
+            /* counter reset */
+            pBuffer = im_buffer;
+            im2col_out_pixel_index++;
+        }
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    uint16_t  i, j, k, l, m, n;
+    int       conv_out;
+    signed char in_row, in_col;
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out; j++)
+        {
+            for (k = 0; k < dim_im_out; k++)
+            {
+                conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                for (m = 0; m < dim_kernel; m++)
+                {
+                    for (n = 0; n < dim_kernel; n++)
+                    {
+                        in_row = stride * j + m - padding;
+                        in_col = stride * k + n - padding;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out +=
+                                    Im_in[(in_row * dim_im_in + in_col) * ch_im_in +
+                                          l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel +
+                                                                                            n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c

@@ -0,0 +1,255 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_convolve_HWC_q15_fast.c
+ * Description:  Fast Q15 version of convolution
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+  /**
+   * @brief Fast Q15 convolution function
+   * @param[in]       Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       wt          pointer to kernel weights
+   * @param[in]       ch_im_out   number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       bias        pointer to bias
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input 
+   * @param[in,out]   bufferB     pointer to buffer space for output
+   * @return     The function returns either
+   * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+   *
+   * bufferB size: 0
+   *
+   * <b>Input dimension constraints:</b>
+   *
+   * ch_im_in is multiple of 2 
+   *
+   * ch_im_out is multipe of 2
+   *
+   */
+
+arm_status
+arm_convolve_HWC_q15_fast(const q15_t * Im_in,
+                          const uint16_t dim_im_in,
+                          const uint16_t ch_im_in,
+                          const q15_t * wt,
+                          const uint16_t ch_im_out,
+                          const uint16_t dim_kernel,
+                          const uint16_t padding,
+                          const uint16_t stride,
+                          const q15_t * bias,
+                          const uint16_t bias_shift,
+                          const uint16_t out_shift,
+                          q15_t * Im_out, 
+                          const uint16_t dim_im_out, 
+                          q15_t * bufferA, 
+                          q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    int16_t   i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+    q15_t    *pBuffer = bufferA;
+    q15_t    *im_buffer = bufferA;
+    q15_t    *pOut = Im_out;
+
+    if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    /* This part implements the im2col function */
+    for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+        {
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        /* arm_copy_q15((q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
+                        memcpy(pBuffer, (q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, sizeof(q15_t)*ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (i_out_x & 0x1)
+            {
+                int       i;
+                /* initialize the matrix pointers for A */
+                const q15_t *pA = wt;
+
+                /* set up the second output pointers */
+                q15_t    *pOut2 = pOut + ch_im_out;
+
+                /* this loop over rows in A */
+                for (i = 0; i < ch_im_out; i += 2)
+                {
+                    /* setup pointers for B */
+                    q15_t    *pB = im_buffer;
+                    const q15_t *pB2 = pB + ch_im_in * dim_kernel * dim_kernel;
+
+                    /* aling the second pointer for A */
+                    const q15_t *pA2 = pA + ch_im_in * dim_kernel * dim_kernel;
+
+                    /* init the sum with bias */
+                    q31_t     sum =  ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                    q31_t     sum2 = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                    q31_t     sum3 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift);
+                    q31_t     sum4 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift);
+
+                    uint16_t  colCnt = ch_im_in * dim_kernel * dim_kernel >> 1;
+                    /* accumulate over the vector */
+                    while (colCnt)
+                    {
+                        q31_t     inA1 = *__SIMD32(pA)++;
+                        q31_t     inB1 = *__SIMD32(pB)++;
+                        q31_t     inA2 = *__SIMD32(pA2)++;
+                        q31_t     inB2 = *__SIMD32(pB2)++;
+
+                        sum = __SMLAD(inA1, inB1, sum);
+                        sum2 = __SMLAD(inA1, inB2, sum2);
+                        sum3 = __SMLAD(inA2, inB1, sum3);
+                        sum4 = __SMLAD(inA2, inB2, sum4);
+
+                        colCnt--;
+                    }           /* while over colCnt */
+                    colCnt = ch_im_in * dim_kernel * dim_kernel & 0x1;
+                    while (colCnt)
+                    {
+                        q15_t     inA1 = *pA++;
+                        q15_t     inB1 = *pB++;
+                        q15_t     inA2 = *pA2++;
+                        q15_t     inB2 = *pB2++;
+
+                        sum += inA1 * inB1;
+                        sum2 += inA1 * inB2;
+                        sum3 += inA2 * inB1;
+                        sum4 += inA2 * inB2;
+                        colCnt--;
+                    }           /* while over colCnt */
+                    *pOut++ = (q15_t) __SSAT(sum >> out_shift, 16);
+                    *pOut++ = (q15_t) __SSAT(sum3 >> out_shift, 16);
+                    *pOut2++ = (q15_t) __SSAT(sum2 >> out_shift, 16);
+                    *pOut2++ = (q15_t) __SSAT(sum4 >> out_shift, 16);
+
+                    /* skip the row computed with A2 */
+                    pA += ch_im_in * dim_kernel * dim_kernel;
+                }               /* for over ch_im_out */
+
+                pOut += ch_im_out;
+                /* counter reset */
+                pBuffer = im_buffer;
+            }
+        }
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    uint16_t  i, j, k, l, m, n;
+    int       conv_out;
+    signed char in_row, in_col;
+
+    if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out; j++)
+        {
+            for (k = 0; k < dim_im_out; k++)
+            {
+                conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                for (m = 0; m < dim_kernel; m++)
+                {
+                    for (n = 0; n < dim_kernel; n++)
+                    {
+                        in_row = stride * j + m - padding;
+                        in_col = stride * k + n - padding;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out +=
+                                    Im_in[(in_row * dim_im_in + in_col) * ch_im_in +
+                                          l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel +
+                                                                                            n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c

@@ -0,0 +1,265 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_convolve_HWC_q15_fast.c
+ * Description:  Fast Q15 version of convolution
+ *
+ * $Date:        24. May 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+  /**
+   * @brief Fast Q15 convolution function (non-sqaure shape)
+   * @param[in]       Im_in        pointer to input tensor
+   * @param[in]       dim_im_in_x  input tensor dimention x
+   * @param[in]       dim_im_in_y  input tensor dimention y
+   * @param[in]       ch_im_in     number of input tensor channels
+   * @param[in]       wt           pointer to kernel weights
+   * @param[in]       ch_im_out    number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel_x filter kernel size x
+   * @param[in]       dim_kernel_y filter kernel size y
+   * @param[in]       padding_x    padding size x
+   * @param[in]       padding_y    padding size y
+   * @param[in]       stride_x     convolution stride x
+   * @param[in]       stride_y     convolution stride y
+   * @param[in]       bias         pointer to bias
+   * @param[in]       bias_shift   amount of left-shift for bias
+   * @param[in]       out_shift    amount of right-shift for output
+   * @param[in,out]   Im_out       pointer to output tensor
+   * @param[in]       dim_im_out_x output tensor dimension x
+   * @param[in]       dim_im_out_y output tensor dimension y
+   * @param[in,out]   bufferA      pointer to buffer space for input 
+   * @param[in,out]   bufferB      pointer to buffer space for output
+   * @return     The function returns either
+   * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+   *
+   * bufferB size: 0
+   *
+   * <b>Input dimension constraints:</b>
+   *
+   * ch_im_in is multiple of 2 
+   *
+   * ch_im_out is multipe of 2
+   *
+   */
+
+arm_status
+arm_convolve_HWC_q15_fast_nonsquare(const q15_t * Im_in,
+                                    const uint16_t dim_im_in_x,
+                                    const uint16_t dim_im_in_y,
+                                    const uint16_t ch_im_in,
+                                    const q15_t * wt,
+                                    const uint16_t ch_im_out,
+                                    const uint16_t dim_kernel_x,
+                                    const uint16_t dim_kernel_y,
+                                    const uint16_t padding_x,
+                                    const uint16_t padding_y,
+                                    const uint16_t stride_x,
+                                    const uint16_t stride_y,
+                                    const q15_t * bias,
+                                    const uint16_t bias_shift,
+                                    const uint16_t out_shift,
+                                    q15_t * Im_out,
+                                    const uint16_t dim_im_out_x,
+                                    const uint16_t dim_im_out_y, 
+                                    q15_t * bufferA, 
+                                    q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    int16_t   i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+    q15_t    *pBuffer = bufferA;
+    q15_t    *im_buffer = bufferA;
+    q15_t    *pOut = Im_out;
+
+    if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    /* This part implements the im2col function */
+    for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+        {
+            for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        /* arm_copy_q15((q15_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
+                        memcpy(pBuffer, (q15_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, sizeof(q15_t)*ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (i_out_x & 0x1)
+            {
+                int       i;
+                /* initialize the matrix pointers for A */
+                const q15_t *pA = wt;
+
+                /* set up the second output pointers */
+                q15_t    *pOut2 = pOut + ch_im_out;
+
+                /* this loop over rows in A */
+                for (i = 0; i < ch_im_out; i += 2)
+                {
+                    /* setup pointers for B */
+                    q15_t    *pB = im_buffer;
+                    const q15_t *pB2 = pB + ch_im_in * dim_kernel_y * dim_kernel_x;
+
+                    /* aling the second pointer for A */
+                    const q15_t *pA2 = pA + ch_im_in * dim_kernel_y * dim_kernel_x;
+
+                    /* init the sum with bias */
+                    q31_t     sum =  ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                    q31_t     sum2 = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                    q31_t     sum3 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift);
+                    q31_t     sum4 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift);
+
+                    uint16_t  colCnt = ch_im_in * dim_kernel_y * dim_kernel_x >> 1;
+                    /* accumulate over the vector */
+                    while (colCnt)
+                    {
+                        q31_t     inA1 = *__SIMD32(pA)++;
+                        q31_t     inB1 = *__SIMD32(pB)++;
+                        q31_t     inA2 = *__SIMD32(pA2)++;
+                        q31_t     inB2 = *__SIMD32(pB2)++;
+
+                        sum = __SMLAD(inA1, inB1, sum);
+                        sum2 = __SMLAD(inA1, inB2, sum2);
+                        sum3 = __SMLAD(inA2, inB1, sum3);
+                        sum4 = __SMLAD(inA2, inB2, sum4);
+
+                        colCnt--;
+                    }           /* while over colCnt */
+                    colCnt = ch_im_in * dim_kernel_y * dim_kernel_x & 0x1;
+                    while (colCnt)
+                    {
+                        q15_t     inA1 = *pA++;
+                        q15_t     inB1 = *pB++;
+                        q15_t     inA2 = *pA2++;
+                        q15_t     inB2 = *pB2++;
+
+                        sum += inA1 * inB1;
+                        sum2 += inA1 * inB2;
+                        sum3 += inA2 * inB1;
+                        sum4 += inA2 * inB2;
+                        colCnt--;
+                    }           /* while over colCnt */
+                    *pOut++ = (q15_t) __SSAT(sum >> out_shift, 16);
+                    *pOut++ = (q15_t) __SSAT(sum3 >> out_shift, 16);
+                    *pOut2++ = (q15_t) __SSAT(sum2 >> out_shift, 16);
+                    *pOut2++ = (q15_t) __SSAT(sum4 >> out_shift, 16);
+
+                    /* skip the row computed with A2 */
+                    pA += ch_im_in * dim_kernel_y * dim_kernel_x;
+                }               /* for over ch_im_out */
+
+                pOut += ch_im_out;
+                /* counter reset */
+                pBuffer = im_buffer;
+            }
+        }
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    uint16_t  i, j, k, l, m, n;
+    int       conv_out;
+    signed char in_row, in_col;
+
+    if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out_y; j++)
+        {
+            for (k = 0; k < dim_im_out_x; k++)
+            {
+                conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                for (m = 0; m < dim_kernel_y; m++)
+                {
+                    for (n = 0; n < dim_kernel_x; n++)
+                    {
+                        in_row = stride_y * j + m - padding_y;
+                        in_col = stride_x * k + n - padding_x;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out +=
+                                    Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in +
+                                          l] * wt[i * ch_im_in * dim_kernel_x * dim_kernel_y + (m * dim_kernel_x +
+                                                                                            n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_RGB.c

@@ -0,0 +1,279 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_convolve_HWC_q7_RGB.c
+ * Description:  Q7 version of convolution for RGB image
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+  /**
+   * @brief Q7 convolution function for RGB image
+   * @param[in]       Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       wt          pointer to kernel weights
+   * @param[in]       ch_im_out   number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       bias        pointer to bias
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input
+   * @param[in,out]   bufferB     pointer to buffer space for output
+   * @return     The function returns either
+   * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+   *
+   * bufferB size: 0
+   *
+   * <b>Input dimension constraints:</b>
+   *
+   * ch_im_in equals 3
+   *
+   * This kernel is written exclusively for convolution with ch_im_in
+   * equals 3. This applies on the first layer of CNNs which has input
+   * image with RGB format.
+   */
+
+arm_status
+arm_convolve_HWC_q7_RGB(const q7_t * Im_in,
+                        const uint16_t dim_im_in,
+                        const uint16_t ch_im_in,
+                        const q7_t * wt,
+                        const uint16_t ch_im_out,
+                        const uint16_t dim_kernel,
+                        const uint16_t padding,
+                        const uint16_t stride,
+                        const q7_t * bias,
+                        const uint16_t bias_shift,
+                        const uint16_t out_shift,
+                        q7_t * Im_out, const uint16_t dim_im_out, q15_t * bufferA, q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+    int16_t   i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+    /*
+     *  Here we use bufferA as q15_t internally as computation are done with q15_t level
+     *  im2col are done to output in q15_t format from q7_t input
+     */
+    q15_t    *pBuffer = bufferA;
+    q7_t     *pOut = Im_out;
+
+    // check if number of input channels is 3
+    if (ch_im_in != 3)
+    {
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+    // This part implements the im2col function
+    for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+        {
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+                    {
+                        /* Equivalent to arm_fill_q15(0, pBuffer, ch_im_in) with assumption: ch_im_in = 3 */
+                        *__SIMD32(pBuffer) = 0x0;
+                        *(pBuffer + 2) = 0;
+                        pBuffer += 3;
+                    } else
+                    {
+                        /* 
+                         * Equivalent to:
+                         *  arm_q7_to_q15_no_shift( (q7_t*)Im_in+(i_ker_y*dim_im_in+i_ker_x)*3, pBuffer, 3);
+                         */
+
+                        const q7_t *pPixel = Im_in + (i_ker_y * dim_im_in + i_ker_x) * 3;
+                        q31_t     buf = *__SIMD32(pPixel);
+
+                        union arm_nnword top;
+                        union arm_nnword bottom;
+
+                        top.word = __SXTB16(buf);
+                        bottom.word = __SXTB16(__ROR(buf, 8));
+
+#ifndef ARM_MATH_BIG_ENDIAN
+                        /*
+                         *  little-endian, | omit | 3rd  | 2nd  | 1st  |
+                         *                MSB                         LSB
+                         *   top | 3rd | 1st |; bottom | omit | 2nd |
+                         *
+                         *  version 1, need to swap 2nd and 3rd weight
+                         * *__SIMD32(pBuffer) = top.word;
+                         * *(pBuffer+2) = bottom.half_words[0];
+                         *
+                         *  version 2, no weight shuffling required
+                         */
+                        *pBuffer++ = top.half_words[0];
+                        *__SIMD32(pBuffer) = __PKHBT(bottom.word, top.word, 0);
+#else
+                        /*
+                         *  big-endian,    | 1st  | 2nd  | 3rd  | omit | 
+                         *                MSB                         LSB
+                         *  top | 2nd | omit |; bottom | 1st | 3rd |
+                         * 
+                         *  version 1, need to swap 2nd and 3rd weight
+                         * *__SIMD32(pBuffer) = bottom.word;
+                         * *(pBuffer+2) = top.half_words[1];
+                         * 
+                         *  version 2, no weight shuffling required
+                         */
+                        *pBuffer++ = bottom.half_words[0];
+                        *__SIMD32(pBuffer) = __PKHTB(top.word, bottom.word, 0);
+#endif
+                        pBuffer += 2;
+                    }
+                }
+            }
+
+            if (pBuffer == bufferA + 2 * 3 * dim_kernel * dim_kernel)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15(wt, bufferA,
+                                                  ch_im_out,
+                                                  3 * dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    /* left-over because odd number of output pixels */
+    if (pBuffer != bufferA)
+    {
+        const q7_t *pA = wt;
+        int       i;
+
+        for (i = 0; i < ch_im_out; i++)
+        {
+            q31_t     sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+            q15_t    *pB = bufferA;
+            /* basically each time it process 4 entries */
+            uint16_t  colCnt = 3 * dim_kernel * dim_kernel >> 2;
+
+            while (colCnt)
+            {
+
+                q31_t     inA1, inA2;
+                q31_t     inB1, inB2;
+
+                pA = (q7_t *) read_and_pad((void *)pA, &inA1, &inA2);
+
+                inB1 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA1, inB1, sum);
+                inB2 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA2, inB2, sum);
+
+                colCnt--;
+            }
+            colCnt = 3 * dim_kernel * dim_kernel & 0x3;
+            while (colCnt)
+            {
+                q7_t      inA1 = *pA++;
+                q15_t     inB1 = *pB++;
+                sum += inA1 * inB1;
+                colCnt--;
+            }
+            *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+        }
+    }
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+    uint16_t  i, j, k, l, m, n;
+    int       conv_out;
+    signed char in_row, in_col;
+
+    // check if number of input channels is 3
+    if (ch_im_in != 3)
+    {
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out; j++)
+        {
+            for (k = 0; k < dim_im_out; k++)
+            {
+                conv_out = (bias[i] << bias_shift) + NN_ROUND(out_shift);
+                for (m = 0; m < dim_kernel; m++)
+                {
+                    for (n = 0; n < dim_kernel; n++)
+                    {
+                        /* if-for implementation */
+                        in_row = stride * j + m - padding;
+                        in_col = stride * k + n - padding;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out +=
+                                    Im_in[(in_row * dim_im_in + in_col) * ch_im_in +
+                                          l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel +
+                                                                                            n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return (ARM_MATH_SUCCESS);
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic.c

@@ -0,0 +1,230 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_convolve_HWC_q7_basic.c
+ * Description:	 Q7 version of convolution
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+  /**
+   * @brief Basic Q7 convolution function
+   * @param[in]       Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       wt          pointer to kernel weights
+   * @param[in]       ch_im_out   number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       bias        pointer to bias
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input 
+   * @param[in,out]   bufferB     pointer to buffer space for output
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code> 
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+   *
+   * bufferB size: 0
+   *
+   * This basic version is designed to work for any input tensor and weight
+   * dimension. 
+   */
+
+arm_status
+arm_convolve_HWC_q7_basic(const q7_t * Im_in,
+                          const uint16_t dim_im_in,
+                          const uint16_t ch_im_in,
+                          const q7_t * wt,
+                          const uint16_t ch_im_out,
+                          const uint16_t dim_kernel,
+                          const uint16_t padding,
+                          const uint16_t stride,
+                          const q7_t * bias,
+                          const uint16_t bias_shift,
+                          const uint16_t out_shift,
+                          q7_t * Im_out, 
+                          const uint16_t dim_im_out, 
+                          q15_t * bufferA, 
+                          q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    int16_t   i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+    /* 
+     *  Here we use bufferA as q15_t internally as computation are done with q15_t level
+     *  im2col are done to output in q15_t format from q7_t input
+     */
+    q15_t    *pBuffer = bufferA;
+    q7_t     *pOut = Im_out;
+
+    /* This part implements the im2col function */
+    for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+        {
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+                    {
+                        /* Filling 0 for out-of-bound paddings */
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        /* Copying the pixel data to column */
+                        arm_q7_to_q15_no_shift((q7_t *)
+                                               Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            /* Computation is filed for every 2 columns */
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15(wt, bufferA,
+                                                  ch_im_out,
+                                                  ch_im_in *
+                                                  dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    /* left-over because odd number of output pixels */
+    if (pBuffer != bufferA)
+    {
+        const q7_t *pA = wt;
+        int       i;
+
+        for (i = 0; i < ch_im_out; i++)
+        {
+            /* Load the accumulator with bias first */
+            q31_t     sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+
+            /* Point to the beging of the im2col buffer */
+            q15_t    *pB = bufferA;
+
+            /* Each time it process 4 entries */
+            uint16_t  colCnt = ch_im_in * dim_kernel * dim_kernel >> 2;
+
+            while (colCnt)
+            {
+                q31_t     inA1, inA2;
+                q31_t     inB1, inB2;
+
+                pA = (q7_t *) read_and_pad((void *)pA, &inA1, &inA2);
+
+                inB1 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA1, inB1, sum);
+                inB2 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA2, inB2, sum);
+
+                colCnt--;
+            }
+            colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3;
+            while (colCnt)
+            {
+                q7_t      inA1 = *pA++;
+                q15_t     inB1 = *pB++;
+                sum += inA1 * inB1;
+                colCnt--;
+            }
+            *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+        }
+    }
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+    uint16_t  i, j, k, l, m, n;
+    int       conv_out;
+    signed char in_row, in_col;
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out; j++)
+        {
+            for (k = 0; k < dim_im_out; k++)
+            {
+                conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                for (m = 0; m < dim_kernel; m++)
+                {
+                    for (n = 0; n < dim_kernel; n++)
+                    {
+                        // if-for implementation
+                        in_row = stride * j + m - padding;
+                        in_col = stride * k + n - padding;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out +=
+                                    Im_in[(in_row * dim_im_in + in_col) * ch_im_in +
+                                          l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel +
+                                                                                            n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic_nonsquare.c

@@ -0,0 +1,228 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_convolve_HWC_q7_basic.c
+ * Description:	 Q7 version of convolution
+ *
+ * $Date:        13. July 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+  /**
+   * @brief Basic Q7 convolution function (non-sqaure shape)
+   * @param[in]       Im_in        pointer to input tensor
+   * @param[in]       dim_im_in_x  input tensor dimention x
+   * @param[in]       dim_im_in_y  input tensor dimention y
+   * @param[in]       ch_im_in     number of input tensor channels
+   * @param[in]       wt           pointer to kernel weights
+   * @param[in]       ch_im_out    number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel_x filter kernel size x
+   * @param[in]       dim_kernel_y filter kernel size y
+   * @param[in]       padding_x    padding size x
+   * @param[in]       padding_y    padding size y
+   * @param[in]       stride_x     convolution stride x
+   * @param[in]       stride_y     convolution stride y
+   * @param[in]       bias         pointer to bias
+   * @param[in]       bias_shift   amount of left-shift for bias
+   * @param[in]       out_shift    amount of right-shift for output
+   * @param[in,out]   Im_out       pointer to output tensor
+   * @param[in]       dim_im_out_x output tensor dimension x
+   * @param[in]       dim_im_out_y output tensor dimension y
+   * @param[in,out]   bufferA      pointer to buffer space for input
+   * @param[in,out]   bufferB      pointer to buffer space for output
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   */
+
+arm_status arm_convolve_HWC_q7_basic_nonsquare(const q7_t * Im_in,
+                                               const uint16_t dim_im_in_x,
+                                               const uint16_t dim_im_in_y,
+                                               const uint16_t ch_im_in,
+                                               const q7_t * wt,
+                                               const uint16_t ch_im_out,
+                                               const uint16_t dim_kernel_x,
+                                               const uint16_t dim_kernel_y,
+                                               const uint16_t padding_x,
+                                               const uint16_t padding_y,
+                                               const uint16_t stride_x,
+                                               const uint16_t stride_y,
+                                               const q7_t * bias,
+                                               const uint16_t bias_shift,
+                                               const uint16_t out_shift,
+                                               q7_t * Im_out,
+                                               const uint16_t dim_im_out_x,
+                                               const uint16_t dim_im_out_y,
+                                               q15_t * bufferA,
+                                               q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    int16_t   i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+    /* 
+     *  Here we use bufferA as q15_t internally as computation are done with q15_t level
+     *  im2col are done to output in q15_t format from q7_t input
+     */
+    q15_t    *pBuffer = bufferA;
+    q7_t     *pOut = Im_out;
+
+    /* This part implements the im2col function */
+    for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+        {
+            for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+                    {
+                        /* Filling 0 for out-of-bound paddings */
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        /* Copying the pixel data to column */
+                        arm_q7_to_q15_no_shift((q7_t *)
+                                               Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            /* Computation is filed for every 2 columns */
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_y * dim_kernel_x)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15(wt, bufferA,
+                                                  ch_im_out,
+                                                  ch_im_in *
+                                                  dim_kernel_y * dim_kernel_x, bias_shift, out_shift, bias, pOut);
+
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    /* left-over because odd number of output pixels */
+    if (pBuffer != bufferA)
+    {
+        const q7_t *pA = wt;
+        int       i;
+
+        for (i = 0; i < ch_im_out; i++)
+        {
+            /* Load the accumulator with bias first */
+            q31_t     sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+
+            /* Point to the beging of the im2col buffer */
+            q15_t    *pB = bufferA;
+
+            /* Each time it process 4 entries */
+            uint16_t  colCnt = ch_im_in * dim_kernel_y * dim_kernel_x >> 2;
+
+            while (colCnt)
+            {
+                q31_t     inA1, inA2;
+                q31_t     inB1, inB2;
+
+                pA = (q7_t *) read_and_pad((void *)pA, &inA1, &inA2);
+
+                inB1 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA1, inB1, sum);
+                inB2 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA2, inB2, sum);
+
+                colCnt--;
+            }
+            colCnt = ch_im_in * dim_kernel_y * dim_kernel_x & 0x3;
+            while (colCnt)
+            {
+                q7_t      inA1 = *pA++;
+                q15_t     inB1 = *pB++;
+                sum += inA1 * inB1;
+                colCnt--;
+            }
+            *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+        }
+    }
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+    uint16_t  i, j, k, l, m, n;
+    int       conv_out;
+    signed char in_row, in_col;
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out_y; j++)
+        {
+            for (k = 0; k < dim_im_out_x; k++)
+            {
+                conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                for (m = 0; m < dim_kernel_y; m++)
+                {
+                    for (n = 0; n < dim_kernel_x; n++)
+                    {
+                        // if-for implementation
+                        in_row = stride_y * j + m - padding_y;
+                        in_col = stride_x * k + n - padding_x;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out +=
+                                    Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] * 
+                                         wt[i * ch_im_in * dim_kernel_y * dim_kernel_x + 
+                                         (m * dim_kernel_x + n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c

@@ -0,0 +1,408 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_convolve_HWC_q7_fast.c
+ * Description:  Fast Q7 version of convolution
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+  /**
+   * @brief Fast Q7 convolution function
+   * @param[in]       Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       wt          pointer to kernel weights
+   * @param[in]       ch_im_out   number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       bias        pointer to bias
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input 
+   * @param[in,out]   bufferB     pointer to buffer space for output
+   * @return     The function returns either
+   * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+   *
+   * bufferB size: 0
+   *
+   * <b>Input dimension constraints:</b>
+   *
+   * ch_im_in is multiple of 4    ( because of the SIMD32 read and swap )
+   *
+   * ch_im_out is multipe of 2    ( bacause 2x2 mat_mult kernel )
+   *
+   * The im2col converts the Q7 tensor input into Q15 column, which is stored in
+   * bufferA. There is reordering happenning during this im2col process with
+   * arm_q7_to_q15_reordered_no_shift. For every four elements, the second and
+   * third elements are swapped. 
+   *
+   * The computation kernel arm_nn_mat_mult_kernel_q7_q15_reordered does the
+   * GEMM computation with the reordered columns.
+   *
+   * To speed-up the determination of the padding condition, we split the
+   * computation into 3x3 parts, i.e., {top, mid, bottom} X {left, mid, right}.
+   * This reduces the total number of boundary condition checks and improves
+   * the data copying performance.
+   */
+
+arm_status
+arm_convolve_HWC_q7_fast(const q7_t * Im_in,
+                         const uint16_t dim_im_in,
+                         const uint16_t ch_im_in,
+                         const q7_t * wt,
+                         const uint16_t ch_im_out,
+                         const uint16_t dim_kernel,
+                         const uint16_t padding,
+                         const uint16_t stride,
+                         const q7_t * bias,
+                         const uint16_t bias_shift,
+                         const uint16_t out_shift,
+                         q7_t * Im_out, 
+                         const uint16_t dim_im_out, 
+                         q15_t * bufferA, 
+                         q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    int16_t   i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+    /*
+     *  Here we use bufferA as q15_t internally as computation are done with q15_t level
+     *  im2col are done to output in q15_t format from q7_t input
+     */
+
+    q15_t    *pBuffer = bufferA;
+    q7_t     *pOut = Im_out;
+
+    if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    /*
+     *  Here we split the entire matrix into three regions depending on the padding situation
+     *    Top: i_out_y from 0 to padding - 1
+     * Middle: i_out_y from padding to dim_im_out-padding-1
+     * Bottom: i_out_y from dim_im_out-padding to dim_im_out-1
+     */
+
+    /* top part */
+    for (i_out_y = 0; i_out_y < padding; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        arm_q7_to_q15_reordered_no_shift
+                            ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
+                                                            bufferA,
+                                                            ch_im_out,
+                                                            ch_im_in
+                                                            *
+                                                            dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    /* middle part, here we also divide the x into left, mid and right */
+    for (; i_out_y < dim_im_out - padding; i_out_y++)
+    {
+
+        /* left part */
+        for (i_out_x = 0; i_out_x < padding; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+                {
+                    if (i_ker_x < 0 || i_ker_x >= dim_im_in)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        arm_q7_to_q15_reordered_no_shift
+                            ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
+                                                            bufferA,
+                                                            ch_im_out,
+                                                            ch_im_in
+                                                            *
+                                                            dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+
+        /* mid part */
+        for (; i_out_x < dim_im_out - padding; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in
+                                                 +
+                                                 (i_ker_y *
+                                                  dim_im_in +
+                                                  i_out_x *
+                                                  stride - padding) * ch_im_in, pBuffer, ch_im_in * dim_kernel);
+                pBuffer += ch_im_in * dim_kernel;
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
+                                                            bufferA,
+                                                            ch_im_out,
+                                                            ch_im_in
+                                                            *
+                                                            dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+
+        /* right part */
+        for (; i_out_x < dim_im_out; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+                {
+                    if (i_ker_x < 0 || i_ker_x >= dim_im_in)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        arm_q7_to_q15_reordered_no_shift
+                            ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
+                                                            bufferA,
+                                                            ch_im_out,
+                                                            ch_im_in
+                                                            *
+                                                            dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    for (; i_out_y < dim_im_out; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        arm_q7_to_q15_reordered_no_shift
+                            ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
+                                                            bufferA,
+                                                            ch_im_out,
+                                                            ch_im_in
+                                                            *
+                                                            dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    /* check if there is left-over for compute */
+    if (pBuffer != bufferA)
+    {
+        const q7_t *pA = wt;
+        int       i;
+
+        for (i = 0; i < ch_im_out; i++)
+        {
+            q31_t     sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+            q15_t    *pB = bufferA;
+            /* each time it process 4 entries */
+            uint16_t  colCnt = ch_im_in * dim_kernel * dim_kernel >> 2;
+
+            while (colCnt)
+            {
+
+                q31_t     inA1, inA2;
+                q31_t     inB1, inB2;
+
+                pA = (q7_t *) read_and_pad_reordered((void *)pA, &inA1, &inA2);
+
+                inB1 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA1, inB1, sum);
+                inB2 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA2, inB2, sum);
+
+                colCnt--;
+            }
+            colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3;
+            while (colCnt)
+            {
+                q7_t      inA1 = *pA++;
+                q15_t     inB1 = *pB++;
+                sum += inA1 * inB1;
+                colCnt--;
+            }
+            *pOut = (q7_t) __SSAT((sum >> out_shift), 8);
+            pOut++;
+
+        }
+
+    }
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+    uint16_t  i, j, k, l, m, n;
+    int       conv_out;
+    signed char in_row, in_col;
+
+    if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out; j++)
+        {
+            for (k = 0; k < dim_im_out; k++)
+            {
+                conv_out = (bias[i] << bias_shift) + NN_ROUND(out_shift);
+                for (m = 0; m < dim_kernel; m++)
+                {
+                    for (n = 0; n < dim_kernel; n++)
+                    {
+                        // if-for implementation
+                        in_row = stride * j + m - padding;
+                        in_col = stride * k + n - padding;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out +=
+                                    Im_in[(in_row * dim_im_in + in_col) * ch_im_in +
+                                          l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel +
+                                                                                            n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast_nonsquare.c

@@ -0,0 +1,379 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_convolve_HWC_q7_fast_nonsquare.c
+ * Description:  Fast Q7 version of convolution (non-sqaure shape)
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+/**
+ * @brief Fast Q7 convolution function (non-sqaure shape)
+ * @param[in]       Im_in        pointer to input tensor
+ * @param[in]       dim_im_in_x  input tensor dimention x
+ * @param[in]       dim_im_in_y  input tensor dimention y
+ * @param[in]       ch_im_in     number of input tensor channels
+ * @param[in]       wt           pointer to kernel weights
+ * @param[in]       ch_im_out    number of filters, i.e., output tensor channels
+ * @param[in]       dim_kernel_x filter kernel size x
+ * @param[in]       dim_kernel_y filter kernel size y
+ * @param[in]       padding_x    padding size x
+ * @param[in]       padding_y    padding size y
+ * @param[in]       stride_x     convolution stride x
+ * @param[in]       stride_y     convolution stride y
+ * @param[in]       bias         pointer to bias
+ * @param[in]       bias_shift   amount of left-shift for bias
+ * @param[in]       out_shift    amount of right-shift for output
+ * @param[in,out]   Im_out       pointer to output tensor
+ * @param[in]       dim_im_out_x output tensor dimension x
+ * @param[in]       dim_im_out_y output tensor dimension y
+ * @param[in,out]   bufferA      pointer to buffer space for input 
+ * @param[in,out]   bufferB      pointer to buffer space for output
+ * @return     The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ *   ch_im_in is multiple of 4
+ *   ch_im_out is multiple of 2
+ */
+
+arm_status arm_convolve_HWC_q7_fast_nonsquare(const q7_t * Im_in,
+                                              const uint16_t dim_im_in_x,
+                                              const uint16_t dim_im_in_y,
+                                              const uint16_t ch_im_in,
+                                              const q7_t * wt,
+                                              const uint16_t ch_im_out,
+                                              const uint16_t dim_kernel_x,
+                                              const uint16_t dim_kernel_y,
+                                              const uint16_t padding_x,
+                                              const uint16_t padding_y,
+                                              const uint16_t stride_x,
+                                              const uint16_t stride_y,
+                                              const q7_t * bias,
+                                              const uint16_t bias_shift,
+                                              const uint16_t out_shift,
+                                              q7_t * Im_out,
+                                              const uint16_t dim_im_out_x,
+                                              const uint16_t dim_im_out_y, 
+                                              q15_t * bufferA, 
+                                              q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    int16_t   i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+    /* -----------------------
+     *  Here we use bufferA as q15_t internally as computation are done with q15_t level
+     *  im2col are done to output in q15_t format from q7_t input
+     */
+
+    q15_t    *pBuffer = bufferA;
+    q7_t     *pOut = Im_out;
+
+    if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    /*
+     *  Here we split the entire matrix into three regions depending on the padding situation
+     *    Top: i_out_y from 0 to padding - 1
+     * Middle: i_out_y from padding to dim_im_out-padding-1
+     * Bottom: i_out_y from dim_im_out-padding to dim_im_out-1
+     */
+
+    /* top part */
+    for (i_out_y = 0; i_out_y < padding_y; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y;
+                 i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x;
+                     i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in,
+                                                         pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y,
+                                                  bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    /* middle part, here we also divide the x into left, mid and right */
+    for (; i_out_y < dim_im_out_y - padding_y; i_out_y++)
+    {
+
+        /* left part */
+        for (i_out_x = 0; i_out_x < padding_x; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y;
+                 i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x;
+                     i_ker_x++)
+                {
+                    if (i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in,
+                                                         pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y,
+                                                  bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+
+        /* mid part */
+        for (; i_out_x < dim_im_out_x - padding_x; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y;
+                 i_ker_y++)
+            {
+                arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in +
+                                                 (i_ker_y * dim_im_in_x + i_out_x * stride_x - padding_x) * ch_im_in,
+                                                 pBuffer, ch_im_in * dim_kernel_x);
+                pBuffer += ch_im_in * dim_kernel_x;
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y,
+                                                  bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+
+        /* right part */
+        for (; i_out_x < dim_im_out_x; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y;
+                 i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x;
+                     i_ker_x++)
+                {
+                    if (i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in,
+                                                         pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y,
+                                                  bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    for (; i_out_y < dim_im_out_y; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y;
+                 i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x;
+                     i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in,
+                                                         pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y,
+                                                  bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    /* check if there is left-over for compute */
+    if (pBuffer != bufferA)
+    {
+        const q7_t *pA = wt;
+        int       i;
+        for (i = 0; i < ch_im_out; i++)
+        {
+            q31_t     sum = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+            q15_t    *pB = bufferA;
+            /* basically each time it process 4 entries */
+            uint16_t  colCnt = ch_im_in * dim_kernel_x * dim_kernel_y >> 2;
+
+            while (colCnt)
+            {
+
+                q31_t     inA1, inA2;
+                q31_t     inB1, inB2;
+
+                pA = (const q7_t *)read_and_pad_reordered((void *)pA, &inA1, &inA2);
+
+                inB1 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA1, inB1, sum);
+                inB2 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA2, inB2, sum);
+
+                colCnt--;
+            }
+            colCnt = (ch_im_in * dim_kernel_y * dim_kernel_x) & 0x3;
+            while (colCnt)
+            {
+                q7_t      inA1 = *pA++;
+                q15_t     inB1 = *pB++;
+                sum += inA1 * inB1;
+                colCnt--;
+            }
+            *pOut = (q7_t) __SSAT((sum >> out_shift), 8);
+            pOut++;
+
+        }
+
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    int       i, j, k, l, m, n;
+    int       conv_out;
+    int       in_row, in_col;
+
+    if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out_y; j++)
+        {
+            for (k = 0; k < dim_im_out_x; k++)
+            {
+                conv_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+                for (m = 0; m < dim_kernel_y; m++)
+                {
+                    for (n = 0; n < dim_kernel_x; n++)
+                    {
+                        /* if-for implementation */
+                        in_row = stride_y * j + m - padding_y;
+                        in_col = stride_x * k + n - padding_x;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] *
+                                    wt[i * ch_im_in * dim_kernel_y * dim_kernel_x + (m * dim_kernel_x + n) * ch_im_in + l];      
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7.c

@@ -0,0 +1,418 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_depthwise_separable_conv_HWC_q7.c
+ * Description:  Q7 depthwise separable convolution function
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+/**
+ * @brief Q7 depthwise separable convolution function
+ * @param[in]       Im_in       pointer to input tensor
+ * @param[in]       dim_im_in   input tensor dimention
+ * @param[in]       ch_im_in    number of input tensor channels
+ * @param[in]       wt          pointer to kernel weights
+ * @param[in]       ch_im_out   number of filters, i.e., output tensor channels
+ * @param[in]       dim_kernel  filter kernel size
+ * @param[in]       padding     padding sizes
+ * @param[in]       stride      convolution stride
+ * @param[in]       bias        pointer to bias
+ * @param[in]       bias_shift  amount of left-shift for bias
+ * @param[in]       out_shift   amount of right-shift for output
+ * @param[in,out]   Im_out      pointer to output tensor
+ * @param[in]       dim_im_out  output tensor dimension
+ * @param[in,out]   bufferA     pointer to buffer space for input
+ * @param[in,out]   bufferB     pointer to buffer space for output
+ * @return     The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+ *
+ * bufferB size: 0
+ *
+ * <b>Input dimension constraints:</b>
+ *
+ * ch_im_in equals ch_im_out
+ *
+ * Implementation:
+ * There are 3 nested loop here:
+ * Inner loop: calculate each output value with MAC instruction over an accumulator
+ * Mid   loop: loop over different output channel
+ * Outer loop: loop over different output (x, y)
+ */
+
+arm_status arm_depthwise_separable_conv_HWC_q7(const q7_t * Im_in,
+                                               const uint16_t dim_im_in,
+                                               const uint16_t ch_im_in,
+                                               const q7_t * wt,
+                                               const uint16_t ch_im_out,
+                                               const uint16_t dim_kernel,
+                                               const uint16_t padding,
+                                               const uint16_t stride,
+                                               const q7_t * bias,
+                                               const uint16_t bias_shift,
+                                               const uint16_t out_shift,
+                                               q7_t * Im_out, 
+                                               const uint16_t dim_im_out, 
+                                               q15_t * bufferA, 
+                                               q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    int16_t   i_out_y, i_out_x;
+    int16_t   i_ker_y, i_ker_x;
+    q7_t     *colBuffer = (q7_t *) bufferA;
+    q7_t     *pBuffer = colBuffer;
+    const q7_t *pBias = bias;
+    q7_t     *pOut = Im_out;
+    uint16_t  rowCnt;
+    uint16_t  row_shift;
+
+    /* do some checking here, basically ch_im_in == ch_im_out */
+    if (ch_im_in != ch_im_out)
+    {
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+        {
+            /* we first do im2col here */
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+                    {
+                        /* arm_fill_q7(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, ch_im_in);
+                    } else
+                    {
+                        /* arm_copy_q7((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
+                        memcpy(pBuffer, (q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            /* we will do the computation here for each channel */
+            rowCnt = ch_im_out >> 2;
+            row_shift = 0;
+            pBias = bias;
+
+            while (rowCnt)
+            {
+                q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+                q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+                q31_t     sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+                q31_t     sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+                uint16_t  colCnt = (dim_kernel * dim_kernel) >> 1;
+                q7_t     *pB = colBuffer + row_shift;
+                const q7_t *pA = wt + row_shift;
+                row_shift += 4;
+
+#ifdef USE_INTRINSIC
+
+#ifndef ARM_MATH_BIG_ENDIAN
+
+                while (colCnt)
+                {
+                    q31_t     inA1, inA2, inB1, inB2, opA, opB;
+
+                    inB1 = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    opB = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    inB2 = __PKHTB(opB, inB1, 16);
+                    inB1 = __PKHBT(inB1, opB, 16);
+                    inA1 = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    opB = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    inA2 = __PKHTB(opB, inA1, 16);
+                    inA1 = __PKHBT(inA1, opB, 16);
+                    opA = __SXTB16(inA1);
+                    opB = __SXTB16(inB1);
+                    sum = __SMLAD(opA, opB, sum);
+                    opA = __SXTB16(__ROR(inA1, 8));
+                    opB = __SXTB16(__ROR(inB1, 8));
+                    sum2 = __SMLAD(opA, opB, sum2);
+                    opA = __SXTB16(inA2);
+                    opB = __SXTB16(inB2);
+                    sum3 = __SMLAD(opA, opB, sum3);
+                    opA = __SXTB16(__ROR(inA2, 8));
+                    opB = __SXTB16(__ROR(inB2, 8));
+                    sum4 = __SMLAD(opA, opB, sum4);
+                    colCnt--;
+                }
+#else
+
+                while (colCnt)
+                {
+                    q31_t     inA1, inA2, inB1, inB2, opA, opB;
+
+                    inB1 = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    opB = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    inB2 = __PKHBT(opB, inB1, 16);
+                    inB1 = __PKHTB(inB1, opB, 16);
+                    inA1 = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    opB = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    inA2 = __PKHBT(opB, inA1, 16);
+                    inA1 = __PKHTB(inA1, opB, 16);
+                    opA = __SXTB16(inA1);
+                    opB = __SXTB16(inB1);
+                    sum2 = __SMLAD(opA, opB, sum2);
+                    opA = __SXTB16(__ROR(inA1, 8));
+                    opB = __SXTB16(__ROR(inB1, 8));
+                    sum = __SMLAD(opA, opB, sum);
+                    opA = __SXTB16(inA2);
+                    opB = __SXTB16(inB2);
+                    sum4 = __SMLAD(opA, opB, sum4);
+                    opA = __SXTB16(__ROR(inA2, 8));
+                    opB = __SXTB16(__ROR(inB2, 8));
+                    sum3 = __SMLAD(opA, opB, sum3);
+                    colCnt--;
+                }
+
+#endif                          /* ARM_MATH_BIG_ENDIAN */
+
+#else
+
+#ifndef ARM_MATH_BIG_ENDIAN
+                /*
+                 *   r0    r1    r2    r3    r4   r5
+                 *  inA1, inA2, inB1, inB2, opA, opB
+                 */
+
+                asm volatile ("COL_LOOP_%=:\n"
+                              "ldr.w r2, [%[pB], #0]\n"
+                              "add.w %[pB], %[pB], %[ch_im_in]\n"
+                              "ldr.w r5, [%[pB], #0]\n"
+                              "add.w %[pB], %[pB], %[ch_im_in]\n"
+                              "pkhtb r3, r5, r2, ASR #16\n"
+                              "pkhbt r2, r2, r5, LSL #16\n"
+                              "ldr.w r0, [%[pA], #0]\n"
+                              "add.w %[pA], %[pA], %[ch_im_in]\n"
+                              "ldr.w r5, [%[pA], #0]\n"
+                              "add.w %[pA], %[pA], %[ch_im_in]\n"
+                              "pkhtb r1, r5, r0, ASR #16\n"
+                              "pkhbt r0, r0, r5, LSL #16\n"
+                              "sxtb16 r4, r0\n"
+                              "sxtb16 r5, r2\n"
+                              "smlad %[sum], r4, r5, %[sum]\n"
+                              "mov.w r4, r0, ror #8\n"
+                              "mov.w r5, r2, ror #8\n"
+                              "sxtb16 r4, r4\n"
+                              "sxtb16 r5, r5\n"
+                              "smlad %[sum2], r4, r5, %[sum2]\n"
+                              "sxtb16 r4, r1\n"
+                              "sxtb16 r5, r3\n"
+                              "smlad %[sum3], r4, r5, %[sum3]\n"
+                              "mov.w r4, r1, ror #8\n"
+                              "mov.w r5, r3, ror #8\n"
+                              "sxtb16 r4, r4\n"
+                              "sxtb16 r5, r5\n"
+                              "smlad %[sum4], r4, r5, %[sum4]\n"
+                              "subs %[colCnt], #1\n"
+                              "bne COL_LOOP_%=\n":[sum]
+                              "+r"(sum),[sum2] "+r"(sum2),
+                              [sum3] "+r"(sum3),
+                              [sum4] "+r"(sum4),[pB] "+r"(pB),
+                              [pA] "+r"(pA):[colCnt]
+                              "r"(colCnt),[ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5");
+#else
+                /*
+                 *  r0    r1    r2    r3    r4   r5
+                 * inA1, inA2, inB1, inB2, opA, opB
+                 */
+                asm volatile ("COL_LOOP_%=:\n"
+                              "ldr.w r2, [%[pB], #0]\n"
+                              "add.w %[pB], %[pB], %[ch_im_in]\n"
+                              "ldr.w r5, [%[pB], #0]\n"
+                              "add.w %[pB], %[pB], %[ch_im_in]\n"
+                              "pkhbt r3, r5, r2, LSL #16\n"
+                              "pkhtb r2, r2, r5, ASR #16\n"
+                              "ldr.w r0, [%[pA], #0]\n"
+                              "add.w %[pA], %[pA], %[ch_im_in]\n"
+                              "ldr.w r5, [%[pA], #0]\n"
+                              "add.w %[pA], %[pA], %[ch_im_in]\n"
+                              "pkhbt r1, r5, r0, LSL #16\n"
+                              "pkhtb r0, r0, r5, ASR #16\n"
+                              "sxtb16 r4, r0\n"
+                              "sxtb16 r5, r2\n"
+                              "smlad %[sum2], r4, r5, %[sum2]\n"
+                              "mov.w r4, r0, ror #8\n"
+                              "mov.w r5, r2, ror #8\n"
+                              "sxtb16 r4, r4\n"
+                              "sxtb16 r5, r5\n"
+                              "smlad %[sum], r4, r5, %[sum]\n"
+                              "sxtb16 r4, r1\n"
+                              "sxtb16 r5, r3\n"
+                              "smlad %[sum4], r4, r5, %[sum4]\n"
+                              "mov.w r4, r1, ror #8\n"
+                              "mov.w r5, r3, ror #8\n"
+                              "sxtb16 r4, r4\n"
+                              "sxtb16 r5, r5\n"
+                              "smlad %[sum3], r4, r5, %[sum3]\n"
+                              "subs %[colCnt], #1\n"
+                              "bne COL_LOOP_%=\n":[sum]
+                              "+r"(sum),[sum2] "+r"(sum2),
+                              [sum3] "+r"(sum3),
+                              [sum4] "+r"(sum4),[pB] "+r"(pB),
+                              [pA] "+r"(pA):[colCnt]
+                              "r"(colCnt),[ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5");
+
+#endif                          /* ARM_MATH_BIG_ENDIAN */
+
+#endif                          /* USE_INTRINSIC */
+
+                colCnt = (dim_kernel * dim_kernel) & 0x1;
+                while (colCnt)
+                {
+                    union arm_nnword inA, inB;
+                    inA.word = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    inB.word = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    sum += inA.bytes[0] * inB.bytes[0];
+                    sum2 += inA.bytes[1] * inB.bytes[1];
+                    sum3 += inA.bytes[2] * inB.bytes[2];
+                    sum4 += inA.bytes[3] * inB.bytes[3];
+                    colCnt--;
+                }
+
+                *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+                *pOut++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+                *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8);
+                *pOut++ = (q7_t) __SSAT((sum4 >> out_shift), 8);
+
+                rowCnt--;
+            }
+
+            rowCnt = ch_im_out & 0x3;
+            while (rowCnt)
+            {
+                q7_t     *pB = colBuffer + row_shift;
+                const q7_t *pA = wt + row_shift;
+                q31_t     sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+                uint16_t  colCnt = (dim_kernel * dim_kernel);
+
+                row_shift += 1;
+
+                while (colCnt)
+                {
+                    q7_t      A1 = *pA;
+                    q7_t      B1 = *pB;
+                    pA += ch_im_in;
+                    pB += ch_im_in;
+                    sum += A1 * B1;
+
+                    colCnt--;
+                }
+                *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+                rowCnt--;
+            }
+
+            /* clear counter and pointers */
+            pBuffer = colBuffer;
+        }
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    int       i_out_y, i_out_x, i_ch_out, i_ker_x, i_ker_y;
+    int       conv_out;
+
+    /* do some checking here, basically ch_im_in == ch_im_out */
+    if (ch_im_in != ch_im_out)
+    {
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+        {
+            for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++)
+            {
+                // for each output
+                conv_out = ((q31_t)(bias[i_ch_out]) << bias_shift) + NN_ROUND(out_shift);
+                for (i_ker_y = 0; i_ker_y < dim_kernel; i_ker_y++)
+                {
+                    for (i_ker_x = 0; i_ker_x < dim_kernel; i_ker_x++)
+                    {
+                        int       in_row = stride * i_out_y + i_ker_y - padding;
+                        int       in_col = stride * i_out_x + i_ker_x - padding;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+                        {
+                            conv_out +=
+                                Im_in[(in_row *
+                                       dim_im_in +
+                                       in_col) *
+                                      ch_im_in +
+                                      i_ch_out] * wt[(i_ker_y * dim_kernel + i_ker_x) * ch_im_out + i_ch_out];
+                        }
+                    }
+                }
+                Im_out[(i_out_y * dim_im_out +
+                        i_out_x) * ch_im_out + i_ch_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7_nonsquare.c

@@ -0,0 +1,411 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_depthwise_separable_conv_HWC_q7_nonsquare.c
+ * Description:  Q7 depthwise separable convolution function (non-square shape)
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+/**
+ * @brief Q7 depthwise separable convolution function (non-square shape)
+ * @param[in]       Im_in         pointer to input tensor
+ * @param[in]       dim_im_in_x   input tensor dimention x
+ * @param[in]       dim_im_in_y   input tensor dimention y
+ * @param[in]       ch_im_in      number of input tensor channels
+ * @param[in]       wt            pointer to kernel weights
+ * @param[in]       ch_im_out     number of filters, i.e., output tensor channels
+ * @param[in]       dim_kernel_x  filter kernel size x
+ * @param[in]       dim_kernel_y  filter kernel size y
+ * @param[in]       padding_x     padding sizes x
+ * @param[in]       padding_y     padding sizes y
+ * @param[in]       stride_x      convolution stride x
+ * @param[in]       stride_y      convolution stride y
+ * @param[in]       bias          pointer to bias
+ * @param[in]       bias_shift    amount of left-shift for bias
+ * @param[in]       out_shift     amount of right-shift for output
+ * @param[in,out]   Im_out        pointer to output tensor
+ * @param[in]       dim_im_out_x  output tensor dimension x
+ * @param[in]       dim_im_out_y  output tensor dimension y
+ * @param[in,out]   bufferA       pointer to buffer space for input 
+ * @param[in,out]   bufferB       pointer to buffer space for output
+ * @return     The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ *   ch_im_in is multiple of 2
+ *   ch_im_out is multiple of 2
+ */
+
+arm_status arm_depthwise_separable_conv_HWC_q7_nonsquare(const q7_t * Im_in,
+                                                         const uint16_t dim_im_in_x,
+                                                         const uint16_t dim_im_in_y,
+                                                         const uint16_t ch_im_in,
+                                                         const q7_t * wt,
+                                                         const uint16_t ch_im_out,
+                                                         const uint16_t dim_kernel_x,
+                                                         const uint16_t dim_kernel_y,
+                                                         const uint16_t padding_x,
+                                                         const uint16_t padding_y,
+                                                         const uint16_t stride_x,
+                                                         const uint16_t stride_y,
+                                                         const q7_t * bias,
+                                                         const uint16_t bias_shift,
+                                                         const uint16_t out_shift,
+                                                         q7_t * Im_out,
+                                                         const uint16_t dim_im_out_x,
+                                                         const uint16_t dim_im_out_y, 
+                                                         q15_t * bufferA, 
+                                                         q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+/*
+ * Implementation:
+ * There are 3 nested loop here:
+ * Inner loop: calculate each output value with MAC instruction over an accumulator
+ * Mid   loop: loop over different output channel
+ * Outer loop: loop over different output (x, y)
+ *
+ */
+
+    int16_t   i_out_y, i_out_x;
+    int16_t   i_ker_y, i_ker_x;
+    q7_t     *colBuffer = (q7_t *) bufferA;
+    q7_t     *pBuffer = colBuffer;
+    const q7_t *pBias = bias;
+    q7_t     *pOut = Im_out;
+    uint16_t  rowCnt;
+    uint16_t  row_shift;
+
+    /* do some checking here, basically ch_im_in == ch_im_out */
+    if (ch_im_in != ch_im_out)
+    {
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+        {
+            /* we first do im2col here */
+            for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y;
+                 i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x;
+                     i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+                    {
+                        /* arm_fill_q7(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, ch_im_in);
+                    } else
+                    {
+                        /* arm_copy_q7((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
+                        memcpy(pBuffer, (q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            /* we will do the computation here for each channel */
+            rowCnt = ch_im_out >> 2;
+            row_shift = 0;
+            pBias = bias;
+
+            while (rowCnt)
+            {
+                q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+                q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+                q31_t     sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+                q31_t     sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+                uint16_t  colCnt = (dim_kernel_x * dim_kernel_y) >> 1;
+                q7_t     *pB = colBuffer + row_shift;
+                const q7_t *pA = wt + row_shift;
+                row_shift += 4;
+
+#ifdef USE_INTRINSIC
+
+#ifndef ARM_MATH_BIG_ENDIAN
+
+                while (colCnt)
+                {
+                    q31_t     inA1, inA2, inB1, inB2, opA, opB;
+
+                    inB1 = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    opB = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    inB2 = __PKHTB(opB, inB1, 16);
+                    inB1 = __PKHBT(inB1, opB, 16);
+                    inA1 = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    opB = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    inA2 = __PKHTB(opB, inA1, 16);
+                    inA1 = __PKHBT(inA1, opB, 16);
+                    opA = __SXTB16(inA1);
+                    opB = __SXTB16(inB1);
+                    sum = __SMLAD(opA, opB, sum);
+                    opA = __SXTB16(__ROR(inA1, 8));
+                    opB = __SXTB16(__ROR(inB1, 8));
+                    sum2 = __SMLAD(opA, opB, sum2);
+                    opA = __SXTB16(inA2);
+                    opB = __SXTB16(inB2);
+                    sum3 = __SMLAD(opA, opB, sum3);
+                    opA = __SXTB16(__ROR(inA2, 8));
+                    opB = __SXTB16(__ROR(inB2, 8));
+                    sum4 = __SMLAD(opA, opB, sum4);
+                    colCnt--;
+                }
+#else
+
+                while (colCnt)
+                {
+                    q31_t     inA1, inA2, inB1, inB2, opA, opB;
+
+                    inB1 = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    opB = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    inB2 = __PKHBT(opB, inB1, 16);
+                    inB1 = __PKHTB(inB1, opB, 16);
+                    inA1 = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    opB = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    inA2 = __PKHBT(opB, inA1, 16);
+                    inA1 = __PKHTB(inA1, opB, 16);
+                    opA = __SXTB16(inA1);
+                    opB = __SXTB16(inB1);
+                    sum2 = __SMLAD(opA, opB, sum2);
+                    opA = __SXTB16(__ROR(inA1, 8));
+                    opB = __SXTB16(__ROR(inB1, 8));
+                    sum = __SMLAD(opA, opB, sum);
+                    opA = __SXTB16(inA2);
+                    opB = __SXTB16(inB2);
+                    sum4 = __SMLAD(opA, opB, sum4);
+                    opA = __SXTB16(__ROR(inA2, 8));
+                    opB = __SXTB16(__ROR(inB2, 8));
+                    sum3 = __SMLAD(opA, opB, sum3);
+                    colCnt--;
+                }
+
+#endif                          /* ARM_MATH_BIG_ENDIAN */
+
+#else
+
+#ifndef ARM_MATH_BIG_ENDIAN
+                //  r0    r1    r2    r3    r4   r5
+                // inA1, inA2, inB1, inB2, opA, opB
+                asm volatile ("COL_LOOP:\n"
+                              "ldr.w r2, [%[pB], #0]\n"
+                              "add.w %[pB], %[pB], %[ch_im_in]\n"
+                              "ldr.w r5, [%[pB], #0]\n"
+                              "add.w %[pB], %[pB], %[ch_im_in]\n"
+                              "pkhtb r3, r5, r2, ASR #16\n"
+                              "pkhbt r2, r2, r5, LSL #16\n"
+                              "ldr.w r0, [%[pA], #0]\n"
+                              "add.w %[pA], %[pA], %[ch_im_in]\n"
+                              "ldr.w r5, [%[pA], #0]\n"
+                              "add.w %[pA], %[pA], %[ch_im_in]\n"
+                              "pkhtb r1, r5, r0, ASR #16\n"
+                              "pkhbt r0, r0, r5, LSL #16\n"
+                              "sxtb16 r4, r0\n"
+                              "sxtb16 r5, r2\n"
+                              "smlad %[sum], r4, r5, %[sum]\n"
+                              "mov.w r4, r0, ror #8\n"
+                              "mov.w r5, r2, ror #8\n"
+                              "sxtb16 r4, r4\n"
+                              "sxtb16 r5, r5\n"
+                              "smlad %[sum2], r4, r5, %[sum2]\n"
+                              "sxtb16 r4, r1\n"
+                              "sxtb16 r5, r3\n"
+                              "smlad %[sum3], r4, r5, %[sum3]\n"
+                              "mov.w r4, r1, ror #8\n"
+                              "mov.w r5, r3, ror #8\n"
+                              "sxtb16 r4, r4\n"
+                              "sxtb16 r5, r5\n"
+                              "smlad %[sum4], r4, r5, %[sum4]\n"
+                              "subs %[colCnt], #1\n"
+                              "bne COL_LOOP\n":[sum] "+r"(sum),[sum2] "+r"(sum2),[sum3] "+r"(sum3),
+                              [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt),
+                              [ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5");
+#else
+                //  r0    r1    r2    r3    r4   r5
+                // inA1, inA2, inB1, inB2, opA, opB
+                asm volatile ("COL_LOOP:\n"
+                              "ldr.w r2, [%[pB], #0]\n"
+                              "add.w %[pB], %[pB], %[ch_im_in]\n"
+                              "ldr.w r5, [%[pB], #0]\n"
+                              "add.w %[pB], %[pB], %[ch_im_in]\n"
+                              "pkhbt r3, r5, r2, LSL #16\n"
+                              "pkhtb r2, r2, r5, ASR #16\n"
+                              "ldr.w r0, [%[pA], #0]\n"
+                              "add.w %[pA], %[pA], %[ch_im_in]\n"
+                              "ldr.w r5, [%[pA], #0]\n"
+                              "add.w %[pA], %[pA], %[ch_im_in]\n"
+                              "pkhbt r1, r5, r0, LSL #16\n"
+                              "pkhtb r0, r0, r5, ASR #16\n"
+                              "sxtb16 r4, r0\n"
+                              "sxtb16 r5, r2\n"
+                              "smlad %[sum2], r4, r5, %[sum2]\n"
+                              "mov.w r4, r0, ror #8\n"
+                              "mov.w r5, r2, ror #8\n"
+                              "sxtb16 r4, r4\n"
+                              "sxtb16 r5, r5\n"
+                              "smlad %[sum], r4, r5, %[sum]\n"
+                              "sxtb16 r4, r1\n"
+                              "sxtb16 r5, r3\n"
+                              "smlad %[sum4], r4, r5, %[sum4]\n"
+                              "mov.w r4, r1, ror #8\n"
+                              "mov.w r5, r3, ror #8\n"
+                              "sxtb16 r4, r4\n"
+                              "sxtb16 r5, r5\n"
+                              "smlad %[sum3], r4, r5, %[sum3]\n"
+                              "subs %[colCnt], #1\n"
+                              "bne COL_LOOP\n":[sum] "+r"(sum),[sum2] "+r"(sum2),[sum3] "+r"(sum3),
+                              [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt),
+                              [ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5");
+#endif                          /*ARM_MATH_BIG_ENDIAN */
+
+#endif                          /* USE_INTRINSIC */
+
+                colCnt = (dim_kernel_x * dim_kernel_y) & 0x1;
+                while (colCnt)
+                {
+                    union arm_nnword inA, inB;
+                    inA.word = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    inB.word = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    sum += inA.bytes[0] * inB.bytes[0];
+                    sum2 += inA.bytes[1] * inB.bytes[1];
+                    sum3 += inA.bytes[2] * inB.bytes[2];
+                    sum4 += inA.bytes[3] * inB.bytes[3];
+                    colCnt--;
+                }
+
+                *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+                *pOut++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+                *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8);
+                *pOut++ = (q7_t) __SSAT((sum4 >> out_shift), 8);
+
+                rowCnt--;
+            }
+
+            rowCnt = ch_im_out & 0x3;
+            while (rowCnt)
+            {
+                q7_t     *pB = colBuffer + row_shift;
+                const q7_t *pA = wt + row_shift;
+                q31_t     sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+                uint16_t  colCnt = (dim_kernel_x * dim_kernel_y);
+
+                row_shift += 1;
+
+                while (colCnt)
+                {
+                    q7_t      A1 = *pA;
+                    q7_t      B1 = *pB;
+                    pA += ch_im_in;
+                    pB += ch_im_in;
+                    sum += A1 * B1;
+
+                    colCnt--;
+                }
+                *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+                rowCnt--;
+            }
+
+            // clear counter and pointers
+            pBuffer = colBuffer;
+        }
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    int       i_out_y, i_out_x, i_ch_out;
+    int       i_ker_y, i_ker_x; 
+
+    /* do some checking here, basically ch_im_in == ch_im_out */
+    if (ch_im_in != ch_im_out)
+    {
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+        {
+            for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++)
+            {
+                // for each output 
+                int       conv_out = ((q31_t)(bias[i_ch_out]) << bias_shift) + NN_ROUND(out_shift);
+                for (i_ker_y = 0; i_ker_y < dim_kernel_y; i_ker_y++)
+                {
+                    for (i_ker_x = 0; i_ker_x < dim_kernel_x; i_ker_x++)
+                    {
+                        int       in_row = stride_y * i_out_y + i_ker_y - padding_y;
+                        int       in_col = stride_x * i_out_x + i_ker_x - padding_x;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+                        {
+                            conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + i_ch_out] *                        
+                                wt[(i_ker_y * dim_kernel_x + i_ker_x) * ch_im_out + i_ch_out];
+                        }
+                    }
+                }
+                Im_out[(i_out_y * dim_im_out_x + i_out_x) * ch_im_out + i_ch_out] =
+                    (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15.c

@@ -0,0 +1,187 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nn_mat_mult_kernel_q7_q15.c
+ * Description:  Matrix-multiplication function for convolution
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+  /**
+   * @brief Matrix-multiplication function for convolution
+   * @param[in]       pA          pointer to operand A
+   * @param[in]       pInBuffer   pointer to operand B, always conssists of 2 vectors
+   * @param[in]       ch_im_out   numRow of A
+   * @param[in]       numCol_A    numCol of A
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        the bias
+   * @param[in,out]   pOut        pointer to output
+   * @return     The function returns the incremented output pointer
+   *
+   * @details
+   *
+   * This function does the matrix multiplication with weight matrix
+   * and 2 columns from im2col. 
+   */
+
+q7_t     *arm_nn_mat_mult_kernel_q7_q15(const q7_t * pA,
+                                        const q15_t * pInBuffer,
+                                        const uint16_t ch_im_out,
+                                        const uint16_t numCol_A,
+                                        const uint16_t bias_shift,
+                                        const uint16_t out_shift, 
+                                        const q7_t * bias, 
+                                        q7_t * pOut)
+{
+#if defined (ARM_MATH_DSP)
+    /* set up the second output pointers */
+    q7_t     *pOut2 = pOut + ch_im_out;
+    const q7_t *pBias = bias;
+
+    uint16_t  rowCnt = ch_im_out >> 1;
+    /* this loop over rows in A */
+    while (rowCnt)
+    {
+        /* setup pointers for B */
+        const q15_t *pB = pInBuffer;
+        const q15_t *pB2 = pB + numCol_A;
+
+        /* align the second pointer for A */
+        const q7_t *pA2 = pA + numCol_A;
+
+        /* init the sum with bias */
+        q31_t     sum =  ((q31_t)(*pBias) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum3 = ((q31_t)(*pBias) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = numCol_A >> 2;
+        /* accumulate over the vector */
+        while (colCnt)
+        {
+            q31_t     inA11, inA12, inA21, inA22;
+            q31_t     inB1 = *__SIMD32(pB)++;
+            q31_t     inB2 = *__SIMD32(pB2)++;
+
+            pA = (q7_t *) read_and_pad((void *)pA, &inA11, &inA12);
+            pA2 = (q7_t *) read_and_pad((void *)pA2, &inA21, &inA22);
+
+            sum = __SMLAD(inA11, inB1, sum);
+            sum2 = __SMLAD(inA11, inB2, sum2);
+            sum3 = __SMLAD(inA21, inB1, sum3);
+            sum4 = __SMLAD(inA21, inB2, sum4);
+
+            inB1 = *__SIMD32(pB)++;
+            inB2 = *__SIMD32(pB2)++;
+
+            sum = __SMLAD(inA12, inB1, sum);
+            sum2 = __SMLAD(inA12, inB2, sum2);
+            sum3 = __SMLAD(inA22, inB1, sum3);
+            sum4 = __SMLAD(inA22, inB2, sum4);
+
+            colCnt--;
+        }                       /* while over colCnt */
+        colCnt = numCol_A & 0x3;
+        while (colCnt)
+        {
+            q7_t      inA1 = *pA++;
+            q15_t     inB1 = *pB++;
+            q7_t      inA2 = *pA2++;
+            q15_t     inB2 = *pB2++;
+
+            sum += inA1 * inB1;
+            sum2 += inA1 * inB2;
+            sum3 += inA2 * inB1;
+            sum4 += inA2 * inB2;
+            colCnt--;
+        }                       /* while over colCnt */
+        *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+        *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8);
+        *pOut2++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+        *pOut2++ = (q7_t) __SSAT((sum4 >> out_shift), 8);
+
+        /* skip the row computed with A2 */
+        pA += numCol_A;
+        rowCnt--;
+    }                           /* for over ch_im_out */
+
+    /* compute left-over row if any */
+    if (ch_im_out & 0x1)
+    {
+        /* setup pointers for B */
+        const q15_t *pB = pInBuffer;
+        const q15_t *pB2 = pB + numCol_A;
+
+        /* load the bias */
+        q31_t     sum = ((q31_t)(*pBias) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = numCol_A >> 2;
+        while (colCnt)
+        {
+            q31_t     inA11, inA12;
+            q31_t     inB1 = *__SIMD32(pB)++;
+            q31_t     inB2 = *__SIMD32(pB2)++;
+
+            pA = (q7_t *) read_and_pad((void *)pA, &inA11, &inA12);
+
+            sum = __SMLAD(inA11, inB1, sum);
+            sum2 = __SMLAD(inA11, inB2, sum2);
+
+            inB1 = *__SIMD32(pB)++;
+            inB2 = *__SIMD32(pB2)++;
+            sum = __SMLAD(inA12, inB1, sum);
+            sum2 = __SMLAD(inA12, inB2, sum2);
+
+            colCnt--;
+        }
+        colCnt = numCol_A & 0x3;
+        while (colCnt)
+        {
+            q7_t      inA1 = *pA++;
+            q15_t     inB1 = *pB++;
+            q15_t     inB2 = *pB2++;
+
+            sum += inA1 * inB1;
+            sum2 += inA1 * inB2;
+            colCnt--;
+        }
+
+        *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+        *pOut2++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+    }
+
+    pOut += ch_im_out;
+
+    /* return the new output pointer with offset */
+    return pOut;
+#else
+    /* To be completed */
+    return NULL;
+#endif                          /* ARM_MATH_DSP */
+
+}

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15_reordered.c

@@ -0,0 +1,138 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nn_mat_mult_kernel_q7_q15_reordered.c
+ * Description:  Matrix-multiplication function for convolution with reordered columns
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnfunctions.h"
+#include "arm_math.h"
+
+  /**
+   * @brief Matrix-multiplication function for convolution with reordered columns
+   * @param[in]       pA          pointer to operand A
+   * @param[in]       pInBuffer   pointer to operand B, always conssists of 2 vectors
+   * @param[in]       ch_im_out   numRow of A
+   * @param[in]       numCol_A    numCol of A
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        the bias
+   * @param[in,out]   pOut        pointer to output
+   * @return     The function returns the incremented output pointer
+   *
+   * @details
+   *
+   * This function assumes that data in pInBuffer are reordered
+   */
+
+q7_t     *arm_nn_mat_mult_kernel_q7_q15_reordered(const q7_t * pA,
+                                                  const q15_t * pInBuffer,
+                                                  const uint16_t ch_im_out,
+                                                  const uint16_t numCol_A,
+                                                  const uint16_t bias_shift,
+                                                  const uint16_t out_shift, 
+                                                  const q7_t * bias, 
+                                                  q7_t * pOut)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* set up the second output pointers */
+    q7_t     *pOut2 = pOut + ch_im_out;
+    int       i;
+
+    /* this loop over rows in A */
+    for (i = 0; i < ch_im_out; i += 2)
+    {
+        /* setup pointers for B */
+        const q15_t *pB = pInBuffer;
+        const q15_t *pB2 = pB + numCol_A;
+
+        /* align the second pointer for A */
+        const q7_t *pA2 = pA + numCol_A;
+
+        /* init the sum with bias */
+        q31_t     sum =  ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum3 = ((q31_t)(bias[i + 1]) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum4 = ((q31_t)(bias[i + 1]) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = numCol_A >> 2;
+        /* accumulate over the vector */
+        while (colCnt)
+        {
+            q31_t     inA11, inA12, inA21, inA22;
+            q31_t     inB1 = *__SIMD32(pB)++;
+            q31_t     inB2 = *__SIMD32(pB2)++;
+
+            pA = (q7_t *) read_and_pad_reordered((void *)pA, &inA11, &inA12);
+            pA2 = (q7_t *) read_and_pad_reordered((void *)pA2, &inA21, &inA22);
+
+            sum = __SMLAD(inA11, inB1, sum);
+            sum2 = __SMLAD(inA11, inB2, sum2);
+            sum3 = __SMLAD(inA21, inB1, sum3);
+            sum4 = __SMLAD(inA21, inB2, sum4);
+
+            inB1 = *__SIMD32(pB)++;
+            inB2 = *__SIMD32(pB2)++;
+
+            sum = __SMLAD(inA12, inB1, sum);
+            sum2 = __SMLAD(inA12, inB2, sum2);
+            sum3 = __SMLAD(inA22, inB1, sum3);
+            sum4 = __SMLAD(inA22, inB2, sum4);
+
+            colCnt--;
+        }                       /* while over colCnt */
+        colCnt = numCol_A & 0x3;
+        while (colCnt)
+        {
+            q7_t      inA1 = *pA++;
+            q15_t     inB1 = *pB++;
+            q7_t      inA2 = *pA2++;
+            q15_t     inB2 = *pB2++;
+
+            sum += inA1 * inB1;
+            sum2 += inA1 * inB2;
+            sum3 += inA2 * inB1;
+            sum4 += inA2 * inB2;
+            colCnt--;
+        }                       /* while over colCnt */
+        *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+        *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8);
+        *pOut2++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+        *pOut2++ = (q7_t) __SSAT((sum4 >> out_shift), 8);
+
+        /* skip the row computed with A2 */
+        pA += numCol_A;
+    }                           /* for over ch_im_out */
+
+    pOut += ch_im_out;
+
+    /* return the new output pointer with offset */
+    return pOut;
+#else
+    /* To be completed */
+    return NULL;
+#endif                          /* ARM_MATH_DSP */
+}

Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15.c

@@ -0,0 +1,199 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_fully_connected_mat_q7_vec_q15.c
+ * Description:  Mixed Q15-Q7 fully-connected layer function
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup FC
+ * @{
+ */
+
+  /**
+   * @brief Mixed Q15-Q7 fully-connected layer function
+   * @param[in]       pV          pointer to input vector
+   * @param[in]       pM          pointer to matrix weights
+   * @param[in]       dim_vec     length of the vector
+   * @param[in]       num_of_rows number of rows in weight matrix
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        pointer to bias
+   * @param[in,out]   pOut        pointer to output vector
+   * @param[in,out]   vec_buffer  pointer to buffer space for input
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * vec_buffer size: 0
+   *
+   *  Q7_Q15 version of the fully connected layer
+   *
+   *  Weights are in q7_t and Activations are in q15_t
+   *
+   */
+
+arm_status
+arm_fully_connected_mat_q7_vec_q15(const q15_t * pV,
+                                   const q7_t * pM,
+                                   const uint16_t dim_vec,
+                                   const uint16_t num_of_rows,
+                                   const uint16_t bias_shift,
+                                   const uint16_t out_shift, 
+                                   const q7_t * bias, 
+                                   q15_t * pOut, 
+                                   q15_t * vec_buffer)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    const q7_t *pB = pM;
+    const q7_t *pB2;
+    q15_t    *pO = pOut;
+    const q7_t *pBias = bias;
+    const q15_t *pA = pV;
+
+    uint16_t  rowCnt = num_of_rows >> 1;
+
+    while (rowCnt)
+    {
+        q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = pV;
+        pB2 = pB + dim_vec;
+
+        while (colCnt)
+        {
+            q31_t     inV, inM11, inM12, inM21, inM22;
+            pB = (q7_t *) read_and_pad((void *)pB, &inM11, &inM12);
+            pB2 = (q7_t *) read_and_pad((void *)pB2, &inM21, &inM22);
+
+            inV = *__SIMD32(pA)++;
+
+            sum = __SMLAD(inV, inM11, sum);
+            sum2 = __SMLAD(inV, inM21, sum2);
+
+            inV = *__SIMD32(pA)++;
+
+            sum = __SMLAD(inV, inM12, sum);
+            sum2 = __SMLAD(inV, inM22, sum2);
+
+            colCnt--;
+        }
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q15_t     inV = *pA++;
+            q7_t      inM = *pB++;
+            q7_t      inM2 = *pB2++;
+
+            sum += inV * inM;
+            sum2 += inV * inM2;
+            colCnt--;
+        }                       /* while over colCnt */
+        *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+        *pO++ = (q15_t) (__SSAT((sum2 >> out_shift), 16));
+
+        /*adjust the pointers and counters */
+        pB += dim_vec;
+        rowCnt--;
+    }
+
+    /* left-over part of the rows */
+    rowCnt = num_of_rows & 0x1;
+
+    while (rowCnt)
+    {
+        q31_t     sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = pV;
+
+        while (colCnt)
+        {
+            q31_t     inV1, inV2, inM11, inM12;
+
+            pB = (q7_t *) read_and_pad((void *)pB, &inM11, &inM12);
+
+            inV1 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV1, inM11, sum);
+
+            inV2 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV2, inM12, sum);
+
+            colCnt--;
+        }
+
+        /* left-over of the vector */
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q15_t     inV = *pA++;
+            q7_t      inM = *pB++;
+            sum += inV * inM;
+            colCnt--;
+        }
+
+        *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+
+        rowCnt--;
+    }
+
+#else
+    int       i, j;
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    for (i = 0; i < num_of_rows; i++)
+    {
+        int       ip_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+        for (j = 0; j < dim_vec; j++)
+        {
+            ip_out += pV[j] * pM[i * dim_vec + j];
+        }
+        pOut[i] = (q15_t) __SSAT((ip_out >> out_shift), 16);
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to ARM_MATH_SUCCESS */
+    return (ARM_MATH_SUCCESS);
+
+}
+
+/**
+ * @} end of FC group
+ */

Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15_opt.c

@@ -0,0 +1,403 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_fully_connected_mat_q7_vec_q15_opt.c
+ * Description:  Mixed Q15-Q7 opt fully-connected layer function
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup FC
+ * @{
+ */
+
+  /**
+   * @brief Mixed Q15-Q7 opt fully-connected layer function
+   * @param[in]       pV          pointer to input vector
+   * @param[in]       pM          pointer to matrix weights
+   * @param[in]       dim_vec     length of the vector
+   * @param[in]       num_of_rows number of rows in weight matrix
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        pointer to bias
+   * @param[in,out]   pOut        pointer to output vector
+   * @param[in,out]   vec_buffer  pointer to buffer space for input
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * vec_buffer size: 0
+   *
+   *  Q7_Q15 version of the fully connected layer
+   *
+   *  Weights are in q7_t and Activations are in q15_t
+   *
+   *  Limitation: x4 version requires weight reordering to work
+   *
+   *  Here we use only one pointer to read 4 rows in the weight
+   *  matrix. So if the original q7_t matrix looks like this:
+   *
+   *  | a11 | a12 | a13 | a14 | a15 | a16 | a17 |
+   *
+   *  | a21 | a22 | a23 | a24 | a25 | a26 | a27 |
+   *
+   *  | a31 | a32 | a33 | a34 | a35 | a36 | a37 |
+   *
+   *  | a41 | a42 | a43 | a44 | a45 | a46 | a47 |
+   *
+   *  | a51 | a52 | a53 | a54 | a55 | a56 | a57 |
+   *
+   *  | a61 | a62 | a63 | a64 | a65 | a66 | a67 |
+   *
+   *  We operates on multiple-of-4 rows, so the first four rows becomes
+   *
+   *  | a11 | a21 | a12 | a22 | a31 | a41 | a32 | a42 |
+   *
+   *  | a13 | a23 | a14 | a24 | a33 | a43 | a34 | a44 |
+   *
+   *  | a15 | a25 | a16 | a26 | a35 | a45 | a36 | a46 |
+   *
+   *  The column left over will be in-order.
+   *  which is:
+   *  | a17 | a27 | a37 | a47 |
+   *
+   *  For the left-over rows, we do 1x1 computation, so the data remains
+   *  as its original order. 
+   *
+   *  So the stored weight matrix looks like this:
+   *
+   *  | a11 | a21 | a12 | a22 | a31 | a41 |
+   *
+   *  | a32 | a42 | a13 | a23 | a14 | a24 |
+   *
+   *  | a33 | a43 | a34 | a44 | a15 | a25 |
+   *
+   *  | a16 | a26 | a35 | a45 | a36 | a46 |
+   *
+   *  | a17 | a27 | a37 | a47 | a51 | a52 |
+   *
+   *  | a53 | a54 | a55 | a56 | a57 | a61 |
+   *
+   *  | a62 | a63 | a64 | a65 | a66 | a67 |
+   *
+   */
+
+arm_status
+arm_fully_connected_mat_q7_vec_q15_opt(const q15_t * pV,
+                                       const q7_t * pM,
+                                       const uint16_t dim_vec,
+                                       const uint16_t num_of_rows,
+                                       const uint16_t bias_shift,
+                                       const uint16_t out_shift, const q7_t * bias, q15_t * pOut, q15_t * vec_buffer)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    const q7_t *pB = pM;
+    q15_t    *pO = pOut;
+    const q7_t *pBias = bias;
+    const q15_t *pA = pV;
+
+    uint16_t  rowCnt = num_of_rows >> 2;
+
+    while (rowCnt)
+    {
+        q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = dim_vec >> 1;
+
+        pA = pV;
+
+#ifdef USE_INTRINSIC
+
+#ifndef ARM_MATH_BIG_ENDIAN
+
+        while (colCnt)
+        {
+            q31_t     inM11, inM12, inM13, inM14;
+            q31_t     inV;
+
+            inV = *__SIMD32(pA)++;
+            inM11 = *__SIMD32(pB)++;
+            inM12 = __SXTB16(__ROR(inM11, 8));
+            inM11 = __SXTB16(inM11);
+            sum = __SMLAD(inM11, inV, sum);
+            sum2 = __SMLAD(inM12, inV, sum2);
+            inM13 = *__SIMD32(pB)++;
+            inM14 = __SXTB16(__ROR(inM13, 8));
+            inM13 = __SXTB16(inM13);
+            sum3 = __SMLAD(inM13, inV, sum3);
+            sum4 = __SMLAD(inM14, inV, sum4);
+            colCnt--;
+        }
+
+#else
+
+        while (colCnt)
+        {
+            q31_t     inM11, inM12, inM13, inM14;
+            q31_t     inV;
+
+            inV = *__SIMD32(pA)++;
+            inM11 = *__SIMD32(pB)++;
+            inM12 = __SXTB16(__ROR(inM11, 8));
+            inM11 = __SXTB16(inM11);
+            sum = __SMLAD(inM12, inV, sum);
+            sum2 = __SMLAD(inM11, inV, sum2);
+            inM13 = *__SIMD32(pB)++;
+            inM14 = __SXTB16(__ROR(inM13, 8));
+            inM13 = __SXTB16(inM13);
+            sum3 = __SMLAD(inM14, inV, sum3);
+            sum4 = __SMLAD(inM13, inV, sum4);
+            colCnt--;
+        }
+
+#endif                          /* ARM_MATH_BIG_ENDIAN */
+
+#else
+
+        /*
+         * register needed:
+         * loop counter: colCnt
+         * accumulators: sum, sum2, sum3, sum4
+         * pointers: pB, pA
+         * weight data: inM11, inM12, inM13, inM14
+         * activation data: inV
+         */
+
+#ifndef ARM_MATH_BIG_ENDIAN
+        asm volatile ("COL_LOOP_%=:\n"
+                      "ldr.w r4, [%[pA]], #4\n"
+                      "ldr.w r1, [%[pB]], #8\n"
+                      "mov.w r0, r1, ror #8\n"
+                      "sxtb16 r0, r0\n"
+                      "sxtb16 r1, r1\n"
+                      "smlad %[sum], r4, r1, %[sum]\n"
+                      "smlad %[sum2], r4, r0, %[sum2]\n"
+                      "ldr.w r3, [%[pB], #-4]\n"
+                      "mov.w r2, r3, ror #8\n"
+                      "sxtb16 r2, r2\n"
+                      "sxtb16 r3, r3\n"
+                      "smlad %[sum3], r4, r3, %[sum3]\n"
+                      "smlad %[sum4], r4, r2, %[sum4]\n"
+                      "subs %[colCnt], #1\n"
+                      "bne COL_LOOP_%=\n":[sum] "+r"(sum),
+                      [sum2] "+r"(sum2),[sum3] "+r"(sum3),
+                      [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4");
+#else
+        asm volatile ("COL_LOOP_%=:\n"
+                      "ldr.w r4, [%[pA]], #4\n"
+                      "ldr.w r1, [%[pB]], #8\n"
+                      "mov.w r0, r1, ror #8\n"
+                      "sxtb16 r0, r0\n"
+                      "sxtb16 r1, r1\n"
+                      "smlad %[sum], r4, r0, %[sum]\n"
+                      "smlad %[sum2], r4, r1, %[sum2]\n"
+                      "ldr.w r3, [%[pB], #-4]\n"
+                      "mov.w r2, r3, ror #8\n"
+                      "sxtb16 r2, r2\n"
+                      "sxtb16 r3, r3\n"
+                      "smlad %[sum3], r4, r2, %[sum3]\n"
+                      "smlad %[sum4], r4, r3, %[sum4]\n"
+                      "subs %[colCnt], #1\n"
+                      "bne COL_LOOP_%=\n":[sum] "+r"(sum),
+                      [sum2] "+r"(sum2),[sum3] "+r"(sum3),
+                      [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4");
+#endif                          /* ARM_MATH_BIG_ENDIAN */
+
+#endif                          /* USE_INTRINSIC */
+
+        colCnt = dim_vec & 0x1;
+        while (colCnt)
+        {
+            q15_t     inV = *pA++;
+            q7_t      inM = *pB++;
+            q7_t      inM2 = *pB++;
+            q7_t      inM3 = *pB++;
+            q7_t      inM4 = *pB++;
+
+            sum += inV * inM;
+            sum2 += inV * inM2;
+            sum3 += inV * inM3;
+            sum4 += inV * inM4;
+            colCnt--;
+        }                       /* while over colCnt */
+        *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+        *pO++ = (q15_t) (__SSAT((sum2 >> out_shift), 16));
+        *pO++ = (q15_t) (__SSAT((sum3 >> out_shift), 16));
+        *pO++ = (q15_t) (__SSAT((sum4 >> out_shift), 16));
+
+        /* adjust the pointers and counters */
+        rowCnt--;
+    }
+
+    /* left-over part of the rows */
+    rowCnt = num_of_rows & 0x3;
+
+    while (rowCnt)
+    {
+        q31_t     sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = pV;
+
+        while (colCnt)
+        {
+            q31_t     inV1, inV2, inM11, inM12;
+
+            pB = (q7_t *) read_and_pad((void *)pB, &inM11, &inM12);
+
+            inV1 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV1, inM11, sum);
+
+            inV2 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV2, inM12, sum);
+
+            colCnt--;
+        }
+
+        /* left-over of the vector */
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q15_t     inV = *pA++;
+            q7_t      inM = *pB++;
+            sum += inV * inM;
+            colCnt--;
+        }
+
+        *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+
+        rowCnt--;
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    uint16_t  rowCnt = num_of_rows >> 2;
+    const q7_t *pB = pM;
+    const q15_t *pA;
+    q15_t    *pO = pOut;
+    const q7_t *pBias = bias;
+
+    while (rowCnt)
+    {
+        q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); 
+        q31_t     sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); 
+        uint16_t  colCnt = dim_vec >> 1;
+
+        pA = pV;
+
+        while (colCnt)
+        {
+            q15_t     inA1 = *pA++;
+            q15_t     inA2 = *pA++;
+
+            q7_t      inB1 = *pB++;
+            q7_t      inB3 = *pB++;
+            q7_t      inB2 = *pB++;
+            q7_t      inB4 = *pB++;
+
+            sum += inA1 * inB1 + inA2 * inB2;
+            sum2 += inA1 * inB3 + inA2 * inB4;
+
+            inB1 = *pB++;
+            inB3 = *pB++;
+            inB2 = *pB++;
+            inB4 = *pB++;
+
+            sum3 += inA1 * inB1 + inA2 * inB2;
+            sum4 += inA1 * inB3 + inA2 * inB4;
+
+            colCnt--;
+        }
+
+        colCnt = dim_vec & 0x1;
+        while (colCnt)
+        {
+            q15_t     inA = *pA++;
+            q7_t      inB = *pB++;
+            sum += inA * inB;
+            inB = *pB++;
+            sum2 += inA * inB;
+            inB = *pB++;
+            sum3 += inA * inB;
+            inB = *pB++;
+            sum4 += inA * inB;
+
+            colCnt--;
+        }
+        *pO++ = (q15_t) __SSAT((sum >> out_shift), 16);
+        *pO++ = (q15_t) __SSAT((sum2 >> out_shift), 16);
+        *pO++ = (q15_t) __SSAT((sum3 >> out_shift), 16);
+        *pO++ = (q15_t) __SSAT((sum4 >> out_shift), 16);
+
+        rowCnt--;
+    }
+
+    rowCnt = num_of_rows & 0x3;
+
+    while (rowCnt)
+    {
+        int       ip_out = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        int       j;
+
+        pA = pV;
+        for (j = 0; j < dim_vec; j++)
+        {
+            q15_t     inA = *pA++;
+            q7_t      inB = *pB++;
+            ip_out += inA * inB;
+        }
+        *pO++ = (q15_t) __SSAT((ip_out >> out_shift), 16);
+
+        rowCnt--;
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to ARM_MATH_SUCCESS */
+    return (ARM_MATH_SUCCESS);
+
+}
+
+/**
+ * @} end of FC group
+ */

Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15.c

@@ -0,0 +1,193 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_fully_connected_q15.c
+ * Description:  Q15 basic fully-connected layer function
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup FC
+ * @{
+ */
+
+  /**
+   * @brief Q15 opt fully-connected layer function
+   * @param[in]       pV          pointer to input vector
+   * @param[in]       pM          pointer to matrix weights
+   * @param[in]       dim_vec     length of the vector
+   * @param[in]       num_of_rows number of rows in weight matrix
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        pointer to bias
+   * @param[in,out]   pOut        pointer to output vector
+   * @param[in,out]   vec_buffer  pointer to buffer space for input
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   *
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * vec_buffer size: 0
+   *
+   */
+
+arm_status
+arm_fully_connected_q15(const q15_t * pV,
+                        const q15_t * pM,
+                        const uint16_t dim_vec,
+                        const uint16_t num_of_rows,
+                        const uint16_t bias_shift,
+                        const uint16_t out_shift, 
+                        const q15_t * bias, 
+                        q15_t * pOut,
+                        q15_t * vec_buffer)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    const q15_t *pB = pM;
+    const q15_t *pB2 = pB + dim_vec;
+    q15_t    *pO = pOut;
+    const q15_t    *pA;
+    const q15_t    *pBias = bias;
+    uint16_t rowCnt = num_of_rows >> 1;
+
+    /* this loop loops over different output */
+    while (rowCnt) {
+        q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = pV;
+        pB2 = pB + dim_vec;
+
+        while (colCnt)
+        {
+            q31_t     inV1, inM1, inM2;
+            inV1 = *__SIMD32(pA)++;
+            inM1 = *__SIMD32(pB)++;
+            sum = __SMLAD(inV1, inM1, sum);
+            inM2 = *__SIMD32(pB2)++;
+            sum2 = __SMLAD(inV1, inM2, sum2);
+
+            inV1 = *__SIMD32(pA)++;
+            inM1 = *__SIMD32(pB)++;
+            sum = __SMLAD(inV1, inM1, sum);
+            inM2 = *__SIMD32(pB2)++;
+            sum2 = __SMLAD(inV1, inM2, sum2);
+
+            colCnt--;
+        }
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q15_t     inV = *pA++;
+            q15_t     inM = *pB++;
+            q15_t     inM2 = *pB2++;
+
+            sum += inV * inM;
+            sum2 += inV * inM2;
+            colCnt--;
+        }                       /* while over colCnt */
+        *pO++ =  (q15_t) (__SSAT((sum >> out_shift), 16));
+        *pO++ = (q15_t) (__SSAT((sum2>> out_shift), 16));
+		
+        /* adjust the pointers and counters */
+        pB = pB + dim_vec;
+        rowCnt --;
+    }
+
+    rowCnt = num_of_rows & 0x1;
+
+    while (rowCnt) {
+        q31_t     sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = pV;
+      
+        while (colCnt) {
+            q31_t     inV1, inM1;
+            inV1 = *__SIMD32(pA)++;
+            inM1 = *__SIMD32(pB)++;
+            sum = __SMLAD(inV1, inM1, sum);
+            
+            inV1 = *__SIMD32(pA)++;
+            inM1 = *__SIMD32(pB)++;
+            sum = __SMLAD(inV1, inM1, sum);
+				
+            colCnt--;
+	}
+			
+	/* left-over of the vector */
+	colCnt = dim_vec & 0x3;
+	while(colCnt) {
+            q15_t     inV = *pA++;
+            q15_t     inM = *pB++;
+
+            sum += inV * inM;
+
+            colCnt--;
+	}
+
+        *pO++ =  (q15_t) (__SSAT((sum >> out_shift), 16));
+			
+        rowCnt --;
+    }
+
+#else
+    int       i, j;
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    for (i = 0; i < num_of_rows; i++)
+    {
+        int       ip_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+        for (j = 0; j < dim_vec; j++)
+        {
+            ip_out += pV[j] * pM[i * dim_vec + j];
+        }
+        pOut[i] = (q15_t) __SSAT((ip_out >> out_shift), 16);
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return (ARM_MATH_SUCCESS);
+
+}
+
+/**
+ * @} end of FC group
+ */

Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15_opt.c

@@ -0,0 +1,332 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_fully_connected_q15_opt.c
+ * Description:  Q15 opt fully-connected layer function
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup FC
+ * @{
+ */
+
+  /**
+   * @brief Q15 opt fully-connected layer function
+   * @param[in]       pV          pointer to input vector
+   * @param[in]       pM          pointer to matrix weights
+   * @param[in]       dim_vec     length of the vector
+   * @param[in]       num_of_rows number of rows in weight matrix
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        pointer to bias
+   * @param[in,out]   pOut        pointer to output vector
+   * @param[in,out]   vec_buffer  pointer to buffer space for input
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   *
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * vec_buffer size: 0
+   *
+   *  Here we use only one pointer to read 4 rows in the weight
+   *  matrix. So if the original matrix looks like this:
+   *
+   *  | a11 | a12 | a13 |
+   *
+   *  | a21 | a22 | a23 |
+   *
+   *  | a31 | a32 | a33 |
+   *
+   *  | a41 | a42 | a43 |
+   *
+   *  | a51 | a52 | a53 |
+   *
+   *  | a61 | a62 | a63 |
+   *
+   *  We operates on multiple-of-4 rows, so the first four rows becomes
+   *
+   *  | a11 | a12 | a21 | a22 | a31 | a32 | a41 | a42 |
+   *
+   *  | a13 | a23 | a33 | a43 |
+   *
+   *  Remaining rows are kept the same original order.
+   *
+   *  So the stored weight matrix looks like this:
+   *
+   *
+   *  | a11 | a12 | a21 | a22 | a31 | a32 | a41 | a42 |
+   *
+   *  | a13 | a23 | a33 | a43 | a51 | a52 | a53 | a61 |
+   *
+   *  | a62 | a63 |
+   */
+
+arm_status
+arm_fully_connected_q15_opt(const q15_t * pV,
+                            const q15_t * pM,
+                            const uint16_t dim_vec,
+                            const uint16_t num_of_rows,
+                            const uint16_t bias_shift,
+                            const uint16_t out_shift, 
+                            const q15_t * bias, 
+                            q15_t * pOut, 
+                            q15_t * vec_buffer)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    const q15_t *pB = pM;
+    q15_t    *pO = pOut;
+    const q15_t *pBias = bias;
+    const q15_t *pA = pV;
+
+    uint16_t  rowCnt = num_of_rows >> 2;
+
+    while (rowCnt)
+    {
+        q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); 
+        q31_t     sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); 
+        q31_t     sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); 
+
+        uint16_t  colCnt = dim_vec >> 1;
+
+        pA = pV;
+
+#ifdef USE_INTRINSIC
+
+        while (colCnt)
+        {
+            q31_t     inM11, inM12, inM13, inM14;
+            q31_t     inV;
+
+            inV = *__SIMD32(pA)++;
+            inM11 = *__SIMD32(pB)++;
+            sum = __SMLAD(inV, inM11, sum);
+            inM12 = *__SIMD32(pB)++;
+            sum2 = __SMLAD(inV, inM12, sum2);
+            inM13 = *__SIMD32(pB)++;
+            sum3 = __SMLAD(inV, inM13, sum3);
+            inM14 = *__SIMD32(pB)++;
+            sum4 = __SMLAD(inV, inM14, sum4);
+            colCnt--;
+        }
+
+#else
+
+        /*
+         * register needed:
+         * loop counter: colCnt
+         * accumulators: sum, sum2, sum3, sum4
+         * pointers: pB, pA
+         * weight data: inM11, inM12, inM13, inM14
+         * activation data: inV
+         */
+
+        asm volatile ("COL_LOOP_%=:\n"
+                      "ldr.w r4, [%[pA]], #4\n"
+                      "ldr.w r0, [%[pB]], #16\n"
+                      "smlad %[sum], r4, r0, %[sum]\n"
+                      "ldr.w r1, [%[pB] , #-12]\n"
+                      "smlad %[sum2], r4, r1, %[sum2]\n"
+                      "ldr.w r2, [%[pB] , #-8]\n"
+                      "smlad %[sum3], r4, r2, %[sum3]\n"
+                      "ldr.w r3, [%[pB] , #-4]\n"
+                      "smlad %[sum4], r4, r3, %[sum4]\n"
+                      "subs %[colCnt], #1\n"
+                      "bne COL_LOOP_%=\n":[sum] "+r"(sum),
+                      [sum2] "+r"(sum2),[sum3] "+r"(sum3),
+                      [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4");
+
+#endif                          /* USE_INTRINSIC */
+
+        colCnt = dim_vec & 0x1;
+        while (colCnt)
+        {
+
+            q15_t     inV = *pA++;
+            q15_t     inM = *pB++;
+            q15_t     inM2 = *pB++;
+            q15_t     inM3 = *pB++;
+            q15_t     inM4 = *pB++;
+
+            sum += inV * inM;
+            sum2 += inV * inM2;
+            sum3 += inV * inM3;
+            sum4 += inV * inM4;
+            colCnt--;
+        }                       /* while over colCnt */
+        *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+        *pO++ = (q15_t) (__SSAT((sum2 >> out_shift), 16));
+        *pO++ = (q15_t) (__SSAT((sum3 >> out_shift), 16));
+        *pO++ = (q15_t) (__SSAT((sum4 >> out_shift), 16));
+
+        /* adjust the pointers and counters */
+        rowCnt--;
+    }
+
+    /* left-over part of the rows */
+    rowCnt = num_of_rows & 0x3;
+
+    while (rowCnt)
+    {
+        q31_t     sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = pV;
+
+        while (colCnt)
+        {
+            q31_t     inV1, inV2, inM1, inM2;
+
+            inM1 = *__SIMD32(pB)++;
+            inV1 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV1, inM1, sum);
+
+            inM2 = *__SIMD32(pB)++;
+            inV2 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV2, inM2, sum);
+
+            colCnt--;
+        }
+
+        /* left-over of the vector */
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q15_t     inV = *pA++;
+            q15_t     inM = *pB++;
+            sum += inV * inM;
+            colCnt--;
+        }
+
+        *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+
+        rowCnt--;
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    uint16_t  rowCnt = num_of_rows >> 2;
+    const q15_t *pB = pM;
+    const q15_t *pA;
+    q15_t    *pO = pOut;
+    const q15_t *pBias = bias;
+
+    while (rowCnt)
+    {
+        q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = dim_vec >> 1;
+
+        pA = pV;
+        while (colCnt)
+        {
+            q15_t     inA1 = *pA++;
+            q15_t     inA2 = *pA++;
+
+            q15_t     inB1 = *pB++;
+            q15_t     inB2 = *pB++;
+            sum += inA1 * inB1 + inA2 * inB2;
+
+            inB1 = *pB++;
+            inB2 = *pB++;
+            sum2 += inA1 * inB1 + inA2 * inB2;
+
+            inB1 = *pB++;
+            inB2 = *pB++;
+            sum3 += inA1 * inB1 + inA2 * inB2;
+
+            inB1 = *pB++;
+            inB2 = *pB++;
+            sum4 += inA1 * inB1 + inA2 * inB2;
+
+            colCnt--;
+        }
+        colCnt = dim_vec & 0x1;
+        while (colCnt)
+        {
+            q15_t     inA = *pA++;
+            q15_t     inB = *pB++;
+            sum += inA * inB;
+            inB = *pB++;
+            sum2 += inA * inB;
+            inB = *pB++;
+            sum3 += inA * inB;
+            inB = *pB++;
+            sum4 += inA * inB;
+            colCnt--;
+        }
+        *pO++ = (q15_t) __SSAT((sum >> out_shift), 16);
+        *pO++ = (q15_t) __SSAT((sum2 >> out_shift), 16);
+        *pO++ = (q15_t) __SSAT((sum3 >> out_shift), 16);
+        *pO++ = (q15_t) __SSAT((sum4 >> out_shift), 16);
+
+        rowCnt--;
+    }
+    rowCnt = num_of_rows & 0x3;
+
+    while (rowCnt)
+    {
+        int       ip_out = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        int       j;
+
+        pA = pV;
+        for (j = 0; j < dim_vec; j++)
+        {
+            q15_t     inA = *pA++;
+            q15_t     inB = *pB++;
+            ip_out += inA * inB;
+        }
+        *pO++ = (q15_t) __SSAT((ip_out >> out_shift), 16);
+
+        rowCnt--;
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to ARM_MATH_SUCCESS */
+    return (ARM_MATH_SUCCESS);
+
+}
+
+/**
+ * @} end of FC group
+ */

Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7.c

@@ -0,0 +1,198 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_fully_connected_q7.c
+ * Description:  Q7 basic fully-connected layer function
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup FC
+ * @{
+ */
+
+  /**
+   * @brief Q7 basic fully-connected layer function
+   * @param[in]       pV          pointer to input vector
+   * @param[in]       pM          pointer to matrix weights
+   * @param[in]       dim_vec     length of the vector
+   * @param[in]       num_of_rows number of rows in weight matrix
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        pointer to bias
+   * @param[in,out]   pOut        pointer to output vector
+   * @param[in,out]   vec_buffer  pointer to buffer space for input
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * vec_buffer size: dim_vec
+   *
+   * This basic function is designed to work with regular weight
+   * matrix without interleaving.
+   *
+   */
+
+arm_status
+arm_fully_connected_q7(const q7_t * pV,
+                       const q7_t * pM,
+                       const uint16_t dim_vec,
+                       const uint16_t num_of_rows,
+                       const uint16_t bias_shift,
+                       const uint16_t out_shift, const q7_t * bias, q7_t * pOut, q15_t * vec_buffer)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    const q7_t *pB = pM;
+    const q7_t *pB2;
+    q7_t     *pO = pOut;
+    const q7_t *pBias = bias;
+    q15_t    *pA;
+    uint16_t  rowCnt = num_of_rows >> 1;
+
+    /* expand the vector into the buffer */
+    arm_q7_to_q15_reordered_no_shift(pV, vec_buffer, dim_vec);
+
+    while (rowCnt)
+    {
+        q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = vec_buffer;
+        pB2 = pB + dim_vec;
+
+        while (colCnt)
+        {
+            q31_t     inV, inM11, inM12, inM21, inM22;
+            pB = (q7_t *) read_and_pad_reordered((void *)pB, &inM11, &inM12);
+            pB2 = (q7_t *) read_and_pad_reordered((void *)pB2, &inM21, &inM22);
+
+            inV = *__SIMD32(pA)++;
+
+            sum = __SMLAD(inV, inM11, sum);
+            sum2 = __SMLAD(inV, inM21, sum2);
+
+            inV = *__SIMD32(pA)++;
+
+            sum = __SMLAD(inV, inM12, sum);
+            sum2 = __SMLAD(inV, inM22, sum2);
+
+            colCnt--;
+        }
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q7_t      inV = *pA++;
+            q15_t     inM = *pB++;
+            q15_t     inM2 = *pB2++;
+
+            sum += inV * inM;
+            sum2 += inV * inM2;
+            colCnt--;
+        }                       /* while over colCnt */
+        *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8));
+        *pO++ = (q7_t) (__SSAT((sum2 >> out_shift), 8));
+
+        /* adjust the pointers and counters */
+        pB += dim_vec;
+        rowCnt--;
+    }
+
+    /* left-over part of the rows */
+    rowCnt = num_of_rows & 0x1;
+
+    while (rowCnt)
+    {
+        uint16_t  colCnt = dim_vec >> 2;
+        q31_t     sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        pA = vec_buffer;
+
+        while (colCnt)
+        {
+            q31_t     inV1, inV2, inM11, inM12;
+
+            pB = (q7_t *) read_and_pad_reordered((void *)pB, &inM11, &inM12);
+
+            inV1 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV1, inM11, sum);
+
+            inV2 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV2, inM12, sum);
+
+            colCnt--;
+        }
+
+        /* left-over of the vector */
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q7_t      inV = *pA++;
+            q15_t     inM = *pB++;
+            sum += inV * inM;
+            colCnt--;
+        }
+
+        *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8));
+
+        rowCnt--;
+    }
+
+#else
+    int       i, j;
+
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    for (i = 0; i < num_of_rows; i++)
+    {
+        int       ip_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+        for (j = 0; j < dim_vec; j++)
+        {
+            ip_out += pV[j] * pM[i * dim_vec + j];
+        }
+        pOut[i] = (q7_t) __SSAT((ip_out >> out_shift), 8);
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to ARM_MATH_SUCCESS */
+    return (ARM_MATH_SUCCESS);
+
+}
+
+/**
+ * @} end of FC group
+ */

Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7_opt.c

@@ -0,0 +1,484 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_fully_connected_q7_opt.c
+ * Description:  Q7 basic fully-connected layer function
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup FC
+ * @{
+ */
+
+  /**
+   * @brief Q7 opt fully-connected layer function
+   * @param[in]       pV          pointer to input vector
+   * @param[in]       pM          pointer to matrix weights
+   * @param[in]       dim_vec     length of the vector
+   * @param[in]       num_of_rows number of rows in weight matrix
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        pointer to bias
+   * @param[in,out]   pOut        pointer to output vector
+   * @param[in,out]   vec_buffer  pointer to buffer space for input
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * vec_buffer size: dim_vec
+   *
+   * This opt function is designed to work with interleaved weight
+   * matrix. The vector input is assumed in q7_t format, we call
+   *  arm_q7_to_q15_no_shift_shuffle function to expand into
+   *  q15_t format with certain weight re-ordering, refer to the function
+   *  comments for more details.
+   *  Here we use only one pointer to read 4 rows in the weight
+   *  matrix. So if the original q7_t matrix looks like this:
+   *
+   *  | a11 | a12 | a13 | a14 | a15 | a16 | a17 |
+   *
+   *  | a21 | a22 | a23 | a24 | a25 | a26 | a27 |
+   *
+   *  | a31 | a32 | a33 | a34 | a35 | a36 | a37 |
+   *
+   *  | a41 | a42 | a43 | a44 | a45 | a46 | a47 |
+   *
+   *  | a51 | a52 | a53 | a54 | a55 | a56 | a57 |
+   *
+   *  | a61 | a62 | a63 | a64 | a65 | a66 | a67 |
+   *
+   *
+   *  We operates on multiple-of-4 rows, so the first four rows becomes
+   *
+   *  | a11 | a21 | a13 | a23 | a31 | a41 | a33 | a43 |
+   *
+   *  | a12 | a22 | a14 | a24 | a32 | a42 | a34 | a44 |
+   *
+   *  | a15 | a25 | a35 | a45 | a16 | a26 | a36 | a46 |
+   *
+   *  So within the kernel, we first read the re-ordered vector in as:
+   *
+   *  | b1  | b3  | and | b2  | b4  |
+   *
+   *  the four q31_t weights will look like
+   *
+   *  | a11 | a13 |, | a21 | a23 |, | a31 | a33 |, | a41 | a43 |
+   *
+   *  | a12 | a14 |, | a22 | a24 |, | a32 | a34 |, | a42 | a44 |
+   *
+   *  The column left over will be in-order.
+   *  which is:
+   *
+   *  | a17 | a27 | a37 | a47 |
+   *
+   *  For the left-over rows, we do 1x1 computation, so the data remains
+   *  as its original order. 
+   *
+   *  So the stored weight matrix looks like this:
+   *
+   *  | a11 | a21 | a13 | a23 | a31 | a41 |
+   *
+   *  | a33 | a43 | a12 | a22 | a14 | a24 |
+   *
+   *  | a32 | a42 | a34 | a44 | a15 | a25 |
+   *
+   *  | a35 | a45 | a16 | a26 | a36 | a46 |
+   *
+   *  | a17 | a27 | a37 | a47 | a51 | a52 |
+   *
+   *  | a53 | a54 | a55 | a56 | a57 | a61 |
+   *
+   *  | a62 | a63 | a64 | a65 | a66 | a67 |
+   *
+   *
+   */
+
+arm_status
+arm_fully_connected_q7_opt(const q7_t * pV,
+                           const q7_t * pM,
+                           const uint16_t dim_vec,
+                           const uint16_t num_of_rows,
+                           const uint16_t bias_shift,
+                           const uint16_t out_shift, 
+                           const q7_t * bias, 
+                           q7_t * pOut, 
+                           q15_t * vec_buffer)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    const q7_t *pB = pM;
+    q7_t     *pO = pOut;
+    const q7_t *pBias = bias;
+    q15_t    *pA;
+    uint16_t  rowCnt = num_of_rows >> 2;
+
+    arm_q7_to_q15_reordered_no_shift(pV, vec_buffer, dim_vec);
+
+    while (rowCnt)
+    {
+
+        q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = vec_buffer;
+
+#ifdef USE_INTRINSIC
+
+#ifndef ARM_MATH_BIG_ENDIAN
+        while (colCnt)
+        {
+            q31_t     inM11, inM12, inM13, inM14;
+            q31_t     inV;
+
+            inV = *__SIMD32(pA)++;
+            inM11 = *__SIMD32(pB)++;
+            inM12 = __SXTB16(__ROR(inM11, 8));
+            inM11 = __SXTB16(inM11);
+            sum = __SMLAD(inM11, inV, sum);
+            sum2 = __SMLAD(inM12, inV, sum2);
+            inM13 = *__SIMD32(pB)++;
+            inM14 = __SXTB16(__ROR(inM13, 8));
+            inM13 = __SXTB16(inM13);
+            sum3 = __SMLAD(inM13, inV, sum3);
+            sum4 = __SMLAD(inM14, inV, sum4);
+
+            inV = *__SIMD32(pA)++;
+            inM11 = *__SIMD32(pB)++;
+            inM12 = __SXTB16(__ROR(inM11, 8));
+            inM11 = __SXTB16(inM11);
+            sum = __SMLAD(inM11, inV, sum);
+            sum2 = __SMLAD(inM12, inV, sum2);
+            inM13 = *__SIMD32(pB)++;
+            inM14 = __SXTB16(__ROR(inM13, 8));
+            inM13 = __SXTB16(inM13);
+            sum3 = __SMLAD(inM13, inV, sum3);
+            sum4 = __SMLAD(inM14, inV, sum4);
+            colCnt--;
+        }
+#else
+        while (colCnt)
+        {
+            q31_t     inM11, inM12, inM13, inM14;
+            q31_t     inV;
+
+            inV = *__SIMD32(pA)++;
+            inM11 = *__SIMD32(pB)++;
+            inM12 = __SXTB16(__ROR(inM11, 8));
+            inM11 = __SXTB16(inM11);
+            sum = __SMLAD(inM12, inV, sum);
+            sum2 = __SMLAD(inM11, inV, sum2);
+            inM13 = *__SIMD32(pB)++;
+            inM14 = __SXTB16(__ROR(inM13, 8));
+            inM13 = __SXTB16(inM13);
+            sum3 = __SMLAD(inM14, inV, sum3);
+            sum4 = __SMLAD(inM13, inV, sum4);
+
+            inV = *__SIMD32(pA)++;
+            inM11 = *__SIMD32(pB)++;
+            inM12 = __SXTB16(__ROR(inM11, 8));
+            inM11 = __SXTB16(inM11);
+            sum = __SMLAD(inM12, inV, sum);
+            sum2 = __SMLAD(inM11, inV, sum2);
+            inM13 = *__SIMD32(pB)++;
+            inM14 = __SXTB16(__ROR(inM13, 8));
+            inM13 = __SXTB16(inM13);
+            sum3 = __SMLAD(inM14, inV, sum3);
+            sum4 = __SMLAD(inM13, inV, sum4);
+            colCnt--;
+        }
+#endif                          /* ARM_MATH_BIG_ENDIAN */
+
+#else
+
+        /*
+         * register needed:
+         * loop counter: colCnt
+         * accumulators: sum, sum2, sum3, sum4
+         * pointers: pB, pA
+         * weight data: inM11, inM12, inM13, inM14
+         * activation data: inV
+         */
+
+#ifndef ARM_MATH_BIG_ENDIAN
+        asm volatile ("COL_LOOP_%=:\n"
+                      "ldr.w r4, [%[pA]], #8\n"
+                      "ldr.w r1, [%[pB]], #16\n"
+                      "mov.w r0, r1, ror #8\n"
+                      "sxtb16 r0, r0\n"
+                      "sxtb16 r1, r1\n"
+                      "smlad %[sum], r4, r1, %[sum]\n"
+                      "smlad %[sum2], r4, r0, %[sum2]\n"
+                      "ldr.w r3, [%[pB], #-12]\n"
+                      "mov.w r2, r3, ror #8\n"
+                      "sxtb16 r2, r2\n"
+                      "sxtb16 r3, r3\n"
+                      "smlad %[sum3], r4, r3, %[sum3]\n"
+                      "smlad %[sum4], r4, r2, %[sum4]\n"
+                      "ldr.w r4, [%[pA], #-4]\n"
+                      "ldr.w r1, [%[pB], #-8]\n"
+                      "mov.w r0, r1, ror #8\n"
+                      "sxtb16 r0, r0\n"
+                      "sxtb16 r1, r1\n"
+                      "smlad %[sum], r4, r1, %[sum]\n"
+                      "smlad %[sum2], r4, r0, %[sum2]\n"
+                      "ldr.w r3, [%[pB], #-4]\n"
+                      "mov.w r2, r3, ror #8\n"
+                      "sxtb16 r2, r2\n"
+                      "sxtb16 r3, r3\n"
+                      "smlad %[sum3], r4, r3, %[sum3]\n"
+                      "smlad %[sum4], r4, r2, %[sum4]\n"
+                      "subs %[colCnt], #1\n"
+                      "bne COL_LOOP_%=\n":[sum] "+r"(sum),
+                      [sum2] "+r"(sum2),[sum3] "+r"(sum3),
+                      [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4");
+#else
+        asm volatile ("COL_LOOP_%=:\n"
+                      "ldr.w r4, [%[pA]], #8\n"
+                      "ldr.w r1, [%[pB]], #16\n"
+                      "mov.w r0, r1, ror #8\n"
+                      "sxtb16 r0, r0\n"
+                      "sxtb16 r1, r1\n"
+                      "smlad %[sum], r4, r0, %[sum]\n"
+                      "smlad %[sum2], r4, r1, %[sum2]\n"
+                      "ldr.w r3, [%[pB], #-12]\n"
+                      "mov.w r2, r3, ror #8\n"
+                      "sxtb16 r2, r2\n"
+                      "sxtb16 r3, r3\n"
+                      "smlad %[sum3], r4, r2, %[sum3]\n"
+                      "smlad %[sum4], r4, r3, %[sum4]\n"
+                      "ldr.w r4, [%[pA], #-4]\n"
+                      "ldr.w r1, [%[pB], #-8]\n"
+                      "mov.w r0, r1, ror #8\n"
+                      "sxtb16 r0, r0\n"
+                      "sxtb16 r1, r1\n"
+                      "smlad %[sum], r4, r0, %[sum]\n"
+                      "smlad %[sum2], r4, r1, %[sum2]\n"
+                      "ldr.w r3, [%[pB], #-4]\n"
+                      "mov.w r2, r3, ror #8\n"
+                      "sxtb16 r2, r2\n"
+                      "sxtb16 r3, r3\n"
+                      "smlad %[sum3], r4, r2, %[sum3]\n"
+                      "smlad %[sum4], r4, r3, %[sum4]\n"
+                      "subs %[colCnt], #1\n"
+                      "bne COL_LOOP_%=\n":[sum] "+r"(sum),
+                      [sum2] "+r"(sum2),[sum3] "+r"(sum3),
+                      [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4");
+#endif                          /* ARM_MATH_BIG_ENDIAN */
+
+#endif                          /* USE_INTRINSIC */
+
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q15_t     inV = *pA++;
+            q7_t      inM = *pB++;
+            q7_t      inM2 = *pB++;
+            q7_t      inM3 = *pB++;
+            q7_t      inM4 = *pB++;
+
+            sum += inV * inM;
+            sum2 += inV * inM2;
+            sum3 += inV * inM3;
+            sum4 += inV * inM4;
+            colCnt--;
+        }                       /* while over colCnt */
+        *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8));
+        *pO++ = (q7_t) (__SSAT((sum2 >> out_shift), 8));
+        *pO++ = (q7_t) (__SSAT((sum3 >> out_shift), 8));
+        *pO++ = (q7_t) (__SSAT((sum4 >> out_shift), 8));
+
+        /* adjust the pointers and counters */
+        rowCnt--;
+    }
+
+    /* left-over part of the rows */
+    rowCnt = num_of_rows & 0x3;
+
+    while (rowCnt)
+    {
+        q31_t     sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = vec_buffer;
+
+        while (colCnt)
+        {
+            q31_t     inV1, inV2, inM11, inM12;
+
+            pB = (q7_t *) read_and_pad_reordered((void *)pB, &inM11, &inM12);
+
+            inV1 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV1, inM11, sum);
+
+            inV2 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV2, inM12, sum);
+
+            colCnt--;
+        }
+
+        /* left-over of the vector */
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q15_t     inV = *pA++;
+            q7_t      inM = *pB++;
+            sum += inV * inM;
+            colCnt--;
+        }
+
+        *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8));
+
+        rowCnt--;
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    uint16_t  rowCnt = num_of_rows >> 2;
+    const q7_t *pB = pM;
+    const q7_t *pA;
+    q7_t     *pO = pOut;
+    const q7_t *pBias = bias;
+
+    while (rowCnt)
+    {
+        q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = pV;
+
+        while (colCnt)
+        {
+            q7_t      inA1 = *pA++;
+            q7_t      inA3 = *pA++;
+            q7_t      inA2 = *pA++;
+            q7_t      inA4 = *pA++;
+
+            q7_t      inB1 = *pB++;
+            q7_t      inB3 = *pB++;
+            q7_t      inB2 = *pB++;
+            q7_t      inB4 = *pB++;
+
+            sum += inA1 * inB1 + inA2 * inB2;
+            sum2 += inA1 * inB3 + inA2 * inB4;
+
+            inB1 = *pB++;
+            inB3 = *pB++;
+            inB2 = *pB++;
+            inB4 = *pB++;
+
+            sum3 += inA1 * inB1 + inA2 * inB2;
+            sum4 += inA1 * inB3 + inA2 * inB4;
+
+            inB1 = *pB++;
+            inB3 = *pB++;
+            inB2 = *pB++;
+            inB4 = *pB++;
+
+            sum += inA3 * inB1 + inA4 * inB2;
+            sum2 += inA3 * inB3 + inA4 * inB4;
+
+            inB1 = *pB++;
+            inB3 = *pB++;
+            inB2 = *pB++;
+            inB4 = *pB++;
+
+            sum3 += inA3 * inB1 + inA4 * inB2;
+            sum4 += inA3 * inB3 + inA4 * inB4;
+
+            colCnt--;
+        }
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q7_t      inA = *pA++;
+            q7_t      inB = *pB++;
+            sum += inA * inB;
+            inB = *pB++;
+            sum2 += inA * inB;
+            inB = *pB++;
+            sum3 += inA * inB;
+            inB = *pB++;
+            sum4 += inA * inB;
+
+            colCnt--;
+        }
+        *pO++ = (q7_t) __SSAT((sum >> out_shift), 8);
+        *pO++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+        *pO++ = (q7_t) __SSAT((sum3 >> out_shift), 8);
+        *pO++ = (q7_t) __SSAT((sum4 >> out_shift), 8);
+
+        rowCnt--;
+    }
+
+    rowCnt = num_of_rows & 0x3;
+
+    while (rowCnt)
+    {
+        int       ip_out = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        int       j;
+
+        pA = pV;
+        for (j = 0; j < dim_vec; j++)
+        {
+            q7_t      inA = *pA++;
+            q7_t      inB = *pB++;
+            ip_out += inA * inB;
+        }
+        *pO++ = (q7_t) __SSAT((ip_out >> out_shift), 8);
+
+        rowCnt--;
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to ARM_MATH_SUCCESS */
+    return (ARM_MATH_SUCCESS);
+
+}
+
+/**
+ * @} end of FC group
+ */

Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q15.c

@@ -0,0 +1,147 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nn_mult_q15.c
+ * Description:  Q15 vector multiplication with variable output shifts
+ *
+ * $Date:        13. July 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnfunctions.h"
+
+/**    
+ * @ingroup groupSupport    
+ */
+
+/**
+ * @addtogroup NNBasicMath
+ * @{
+ */
+
+
+/**
+ * @brief           Q7 vector multiplication with variable output shifts
+ * @param[in]       *pSrcA        pointer to the first input vector
+ * @param[in]       *pSrcB        pointer to the second input vector
+ * @param[out]      *pDst         pointer to the output vector
+ * @param[in]       out_shift     amount of right-shift for output
+ * @param[in]       blockSize     number of samples in each vector
+ * @return none.
+ *
+ * <b>Scaling and Overflow Behavior:</b>
+ * \par
+ * The function uses saturating arithmetic.
+ * Results outside of the allowable Q15 range [0x8000 0x7FFF] will be saturated.
+ */
+
+void arm_nn_mult_q15(
+  q15_t * pSrcA,
+  q15_t * pSrcB,
+  q15_t * pDst,
+  const uint16_t out_shift,
+  uint32_t blockSize)
+{
+  uint32_t blkCnt;                               /* loop counters */
+
+#if defined (ARM_MATH_DSP)
+
+/* Run the below code for Cortex-M4 and Cortex-M3 */
+  q31_t inA1, inA2, inB1, inB2;                  /* temporary input variables */
+  q15_t out1, out2, out3, out4;                  /* temporary output variables */
+  q31_t mul1, mul2, mul3, mul4;                  /* temporary variables */
+
+  /* loop Unrolling */
+  blkCnt = blockSize >> 2U;
+
+  /* First part of the processing with loop unrolling.  Compute 4 outputs at a time.
+   ** a second loop below computes the remaining 1 to 3 samples. */
+  while (blkCnt > 0U)
+  {
+    /* read two samples at a time from sourceA */
+    inA1 = *__SIMD32(pSrcA)++;
+    /* read two samples at a time from sourceB */
+    inB1 = *__SIMD32(pSrcB)++;
+    /* read two samples at a time from sourceA */
+    inA2 = *__SIMD32(pSrcA)++;
+    /* read two samples at a time from sourceB */
+    inB2 = *__SIMD32(pSrcB)++;
+
+    /* multiply mul = sourceA * sourceB */
+    mul1 = (q31_t) ((q15_t) (inA1 >> 16) * (q15_t) (inB1 >> 16));
+    mul2 = (q31_t) ((q15_t) inA1 * (q15_t) inB1);
+    mul3 = (q31_t) ((q15_t) (inA2 >> 16) * (q15_t) (inB2 >> 16));
+    mul4 = (q31_t) ((q15_t) inA2 * (q15_t) inB2);
+
+    /* saturate result to 16 bit */
+    out1 = (q15_t) __SSAT((mul1 + NN_ROUND(out_shift)) >> out_shift, 16);
+    out2 = (q15_t) __SSAT((mul2 + NN_ROUND(out_shift)) >> out_shift, 16);
+    out3 = (q15_t) __SSAT((mul3 + NN_ROUND(out_shift)) >> out_shift, 16);
+    out4 = (q15_t) __SSAT((mul4 + NN_ROUND(out_shift)) >> out_shift, 16);
+
+    /* store the result */
+#ifndef ARM_MATH_BIG_ENDIAN
+
+    *__SIMD32(pDst)++ = __PKHBT(out2, out1, 16);
+    *__SIMD32(pDst)++ = __PKHBT(out4, out3, 16);
+
+#else
+
+    *__SIMD32(pDst)++ = __PKHBT(out2, out1, 16);
+    *__SIMD32(pDst)++ = __PKHBT(out4, out3, 16);
+
+#endif /* #ifndef ARM_MATH_BIG_ENDIAN */
+
+    /* Decrement the blockSize loop counter */
+    blkCnt--;
+  }
+
+  /* If the blockSize is not a multiple of 4, compute any remaining output samples here.
+   ** No loop unrolling is used. */
+  blkCnt = blockSize % 0x4U;
+
+#else
+
+  /* Run the below code for Cortex-M0 */
+
+  /* Initialize blkCnt with number of samples */
+  blkCnt = blockSize;
+
+#endif /* #if defined (ARM_MATH_DSP) */
+
+
+  while (blkCnt > 0U)
+  {
+    /* C = A * B */
+    /* Multiply the inputs and store the result in the destination buffer */
+    *pDst++ = (q15_t) __SSAT((((q31_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 16);
+
+    /* Decrement the blockSize loop counter */
+    blkCnt--;
+  }
+}
+
+/**
+ * @} end of NNBasicMath group
+ */
+

Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q7.c

@@ -0,0 +1,119 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nn_mult_q7.c
+ * Description:  Q7 vector multiplication with variable output shifts
+ *
+ * $Date:        13. July 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnfunctions.h"
+
+/**    
+ * @ingroup groupSupport    
+ */
+
+/**
+ * @addtogroup NNBasicMath
+ * @{
+ */
+
+/**
+ * @brief           Q7 vector multiplication with variable output shifts
+ * @param[in]       *pSrcA        pointer to the first input vector
+ * @param[in]       *pSrcB        pointer to the second input vector
+ * @param[out]      *pDst         pointer to the output vector
+ * @param[in]       out_shift     amount of right-shift for output
+ * @param[in]       blockSize     number of samples in each vector
+ * @return none.
+ *
+ * <b>Scaling and Overflow Behavior:</b>
+ * \par
+ * The function uses saturating arithmetic.
+ * Results outside of the allowable Q7 range [0x80 0x7F] will be saturated.
+ */
+
+void arm_nn_mult_q7(
+  q7_t * pSrcA,
+  q7_t * pSrcB,
+  q7_t * pDst,
+  const uint16_t out_shift,
+  uint32_t blockSize)
+{
+  uint32_t blkCnt;                               /* loop counters */
+
+#if defined (ARM_MATH_DSP)
+
+/* Run the below code for Cortex-M4 and Cortex-M3 */
+  q7_t out1, out2, out3, out4;                   /* Temporary variables to store the product */
+
+  /* loop Unrolling */
+  blkCnt = blockSize >> 2U;
+
+  /* First part of the processing with loop unrolling.  Compute 4 outputs at a time.
+   ** a second loop below computes the remaining 1 to 3 samples. */
+  while (blkCnt > 0U)
+  {
+    /* C = A * B */
+    /* Multiply the inputs and store the results in temporary variables */
+    out1 = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8);
+    out2 = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8);
+    out3 = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8);
+    out4 = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8);
+
+    /* Store the results of 4 inputs in the destination buffer in single cycle by packing */
+    *__SIMD32(pDst)++ = __PACKq7(out1, out2, out3, out4);
+
+    /* Decrement the blockSize loop counter */
+    blkCnt--;
+  }
+
+  /* If the blockSize is not a multiple of 4, compute any remaining output samples here.
+   ** No loop unrolling is used. */
+  blkCnt = blockSize % 0x4U;
+
+#else
+
+  /* Run the below code for Cortex-M0 */
+
+  /* Initialize blkCnt with number of samples */
+  blkCnt = blockSize;
+
+#endif /* #if defined (ARM_MATH_DSP) */
+
+
+  while (blkCnt > 0U)
+  {
+    /* C = A * B */
+    /* Multiply the inputs and store the result in the destination buffer */
+    *pDst++ = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8);
+
+    /* Decrement the blockSize loop counter */
+    blkCnt--;
+  }
+}
+
+/**
+ * @} end of NNBasicMath group
+ */

Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_nntables.c

@@ -0,0 +1,297 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nntables.c
+ * Description:  Converts the elements of the Q7 vector to Q15 vector without left-shift
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnsupportfunctions.h"
+
+/**
+ * @brief tables for various activation functions
+ *
+ * This file include the declaration of common tables.
+ * Most of them are used for activation functions 
+ *
+ * Assumption:
+ * Unified table: input is 3.x format, i.e, range of [-8, 8)
+ * sigmoid(8) = 0.9996646498695336
+ * tanh(8) = 0.9999997749296758
+ * The accuracy here should be good enough
+ *
+ * 2-stage HL table: 
+ *
+ * The entire input range is divided into two parts:
+ *
+ * Low range table: 0x000x xxxx or 0x111x xxxx 
+ * table entry will be the binary number excluding the first
+ * two digits, i.e., 0x0x xxxx or 0x1x xxxx
+ * 
+ *
+ *
+ * High range table 0x0010 0000 -- 0x0111 1111
+ *                  0x1000 0000 -- 0x1101 1111
+ * 
+ * For positive numbers, table entry will be
+ * 0x0010 0000 -- 0x0111 1111 minus 0x0010 0000
+ * i.e., 0x0000 0000 - 0x0101 11111
+ *
+ * same thing for the negative numbers, table entry will be
+ * 0x1000 0000 -- 0x1101 1111 minux 0x0010 0000
+ * i.e., 0x0110 0000 - 0x1011 1111
+ */
+
+const q7_t sigmoidTable_q7[256] = {
+    0x40, 0x42, 0x44, 0x46, 0x48, 0x4a, 0x4c, 0x4e,
+    0x50, 0x52, 0x53, 0x55, 0x57, 0x59, 0x5a, 0x5c,
+    0x5e, 0x5f, 0x61, 0x62, 0x63, 0x65, 0x66, 0x67,
+    0x69, 0x6a, 0x6b, 0x6c, 0x6d, 0x6e, 0x6f, 0x70,
+    0x71, 0x72, 0x72, 0x73, 0x74, 0x74, 0x75, 0x76,
+    0x76, 0x77, 0x77, 0x78, 0x78, 0x79, 0x79, 0x7a,
+    0x7a, 0x7a, 0x7b, 0x7b, 0x7b, 0x7c, 0x7c, 0x7c,
+    0x7c, 0x7c, 0x7d, 0x7d, 0x7d, 0x7d, 0x7d, 0x7e,
+    0x7e, 0x7e, 0x7e, 0x7e, 0x7e, 0x7e, 0x7e, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
+    0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
+    0x01, 0x01, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02,
+    0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x04,
+    0x04, 0x04, 0x04, 0x04, 0x05, 0x05, 0x05, 0x06,
+    0x06, 0x06, 0x07, 0x07, 0x08, 0x08, 0x09, 0x09,
+    0x0a, 0x0a, 0x0b, 0x0c, 0x0c, 0x0d, 0x0e, 0x0e,
+    0x0f, 0x10, 0x11, 0x12, 0x13, 0x14, 0x15, 0x16,
+    0x17, 0x19, 0x1a, 0x1b, 0x1d, 0x1e, 0x1f, 0x21,
+    0x22, 0x24, 0x26, 0x27, 0x29, 0x2b, 0x2d, 0x2e,
+    0x30, 0x32, 0x34, 0x36, 0x38, 0x3a, 0x3c, 0x3e,
+};
+
+const q15_t sigmoidTable_q15[256] = {
+    0x4000, 0x4200, 0x43ff, 0x45fc, 0x47f5, 0x49eb, 0x4bdc, 0x4dc8,
+    0x4fad, 0x518a, 0x5360, 0x552c, 0x56ef, 0x58a8, 0x5a57, 0x5bfb,
+    0x5d93, 0x5f20, 0x60a1, 0x6216, 0x637f, 0x64db, 0x662b, 0x676f,
+    0x68a6, 0x69d2, 0x6af1, 0x6c05, 0x6d0d, 0x6e09, 0x6efb, 0x6fe2,
+    0x70be, 0x7190, 0x7258, 0x7316, 0x73cc, 0x7478, 0x751b, 0x75b7,
+    0x764a, 0x76d6, 0x775b, 0x77d8, 0x784f, 0x78c0, 0x792a, 0x798f,
+    0x79ee, 0x7a48, 0x7a9d, 0x7aed, 0x7b39, 0x7b80, 0x7bc4, 0x7c03,
+    0x7c3f, 0x7c78, 0x7cad, 0x7ce0, 0x7d0f, 0x7d3c, 0x7d66, 0x7d8d,
+    0x7db3, 0x7dd6, 0x7df7, 0x7e16, 0x7e33, 0x7e4f, 0x7e69, 0x7e81,
+    0x7e98, 0x7eae, 0x7ec2, 0x7ed5, 0x7ee7, 0x7ef8, 0x7f08, 0x7f17,
+    0x7f25, 0x7f32, 0x7f3e, 0x7f4a, 0x7f55, 0x7f5f, 0x7f69, 0x7f72,
+    0x7f7b, 0x7f83, 0x7f8a, 0x7f91, 0x7f98, 0x7f9e, 0x7fa4, 0x7faa,
+    0x7faf, 0x7fb4, 0x7fb8, 0x7fbd, 0x7fc1, 0x7fc5, 0x7fc8, 0x7fcc,
+    0x7fcf, 0x7fd2, 0x7fd5, 0x7fd7, 0x7fda, 0x7fdc, 0x7fde, 0x7fe0,
+    0x7fe2, 0x7fe4, 0x7fe6, 0x7fe7, 0x7fe9, 0x7fea, 0x7feb, 0x7fed,
+    0x7fee, 0x7fef, 0x7ff0, 0x7ff1, 0x7ff2, 0x7ff3, 0x7ff4, 0x7ff4,
+    0x000b, 0x000c, 0x000c, 0x000d, 0x000e, 0x000f, 0x0010, 0x0011,
+    0x0012, 0x0013, 0x0015, 0x0016, 0x0017, 0x0019, 0x001a, 0x001c,
+    0x001e, 0x0020, 0x0022, 0x0024, 0x0026, 0x0029, 0x002b, 0x002e,
+    0x0031, 0x0034, 0x0038, 0x003b, 0x003f, 0x0043, 0x0048, 0x004c,
+    0x0051, 0x0056, 0x005c, 0x0062, 0x0068, 0x006f, 0x0076, 0x007d,
+    0x0085, 0x008e, 0x0097, 0x00a1, 0x00ab, 0x00b6, 0x00c2, 0x00ce,
+    0x00db, 0x00e9, 0x00f8, 0x0108, 0x0119, 0x012b, 0x013e, 0x0152,
+    0x0168, 0x017f, 0x0197, 0x01b1, 0x01cd, 0x01ea, 0x0209, 0x022a,
+    0x024d, 0x0273, 0x029a, 0x02c4, 0x02f1, 0x0320, 0x0353, 0x0388,
+    0x03c1, 0x03fd, 0x043c, 0x0480, 0x04c7, 0x0513, 0x0563, 0x05b8,
+    0x0612, 0x0671, 0x06d6, 0x0740, 0x07b1, 0x0828, 0x08a5, 0x092a,
+    0x09b6, 0x0a49, 0x0ae5, 0x0b88, 0x0c34, 0x0cea, 0x0da8, 0x0e70,
+    0x0f42, 0x101e, 0x1105, 0x11f7, 0x12f3, 0x13fb, 0x150f, 0x162e,
+    0x175a, 0x1891, 0x19d5, 0x1b25, 0x1c81, 0x1dea, 0x1f5f, 0x20e0,
+    0x226d, 0x2405, 0x25a9, 0x2758, 0x2911, 0x2ad4, 0x2ca0, 0x2e76,
+    0x3053, 0x3238, 0x3424, 0x3615, 0x380b, 0x3a04, 0x3c01, 0x3e00,
+};
+
+const q15_t sigmoidLTable_q15[128] = {
+    0x4000, 0x4100, 0x4200, 0x42ff, 0x43ff, 0x44fd, 0x45fc, 0x46f9,
+    0x47f5, 0x48f1, 0x49eb, 0x4ae5, 0x4bdc, 0x4cd3, 0x4dc8, 0x4ebb,
+    0x4fad, 0x509c, 0x518a, 0x5276, 0x5360, 0x5447, 0x552c, 0x560f,
+    0x56ef, 0x57cd, 0x58a8, 0x5981, 0x5a57, 0x5b2a, 0x5bfb, 0x5cc9,
+    0x5d93, 0x5e5b, 0x5f20, 0x5fe2, 0x60a1, 0x615d, 0x6216, 0x62cc,
+    0x637f, 0x642e, 0x64db, 0x6584, 0x662b, 0x66ce, 0x676f, 0x680c,
+    0x68a6, 0x693d, 0x69d2, 0x6a63, 0x6af1, 0x6b7c, 0x6c05, 0x6c8a,
+    0x6d0d, 0x6d8d, 0x6e09, 0x6e84, 0x6efb, 0x6f70, 0x6fe2, 0x7051,
+    0x0f42, 0x0faf, 0x101e, 0x1090, 0x1105, 0x117c, 0x11f7, 0x1273,
+    0x12f3, 0x1376, 0x13fb, 0x1484, 0x150f, 0x159d, 0x162e, 0x16c3,
+    0x175a, 0x17f4, 0x1891, 0x1932, 0x19d5, 0x1a7c, 0x1b25, 0x1bd2,
+    0x1c81, 0x1d34, 0x1dea, 0x1ea3, 0x1f5f, 0x201e, 0x20e0, 0x21a5,
+    0x226d, 0x2337, 0x2405, 0x24d6, 0x25a9, 0x267f, 0x2758, 0x2833,
+    0x2911, 0x29f1, 0x2ad4, 0x2bb9, 0x2ca0, 0x2d8a, 0x2e76, 0x2f64,
+    0x3053, 0x3145, 0x3238, 0x332d, 0x3424, 0x351b, 0x3615, 0x370f,
+    0x380b, 0x3907, 0x3a04, 0x3b03, 0x3c01, 0x3d01, 0x3e00, 0x3f00,
+};
+
+const q15_t sigmoidHTable_q15[192] = {
+    0x70be, 0x7190, 0x7258, 0x7316, 0x73cc, 0x7478, 0x751b, 0x75b7,
+    0x764a, 0x76d6, 0x775b, 0x77d8, 0x784f, 0x78c0, 0x792a, 0x798f,
+    0x79ee, 0x7a48, 0x7a9d, 0x7aed, 0x7b39, 0x7b80, 0x7bc4, 0x7c03,
+    0x7c3f, 0x7c78, 0x7cad, 0x7ce0, 0x7d0f, 0x7d3c, 0x7d66, 0x7d8d,
+    0x7db3, 0x7dd6, 0x7df7, 0x7e16, 0x7e33, 0x7e4f, 0x7e69, 0x7e81,
+    0x7e98, 0x7eae, 0x7ec2, 0x7ed5, 0x7ee7, 0x7ef8, 0x7f08, 0x7f17,
+    0x7f25, 0x7f32, 0x7f3e, 0x7f4a, 0x7f55, 0x7f5f, 0x7f69, 0x7f72,
+    0x7f7b, 0x7f83, 0x7f8a, 0x7f91, 0x7f98, 0x7f9e, 0x7fa4, 0x7faa,
+    0x7faf, 0x7fb4, 0x7fb8, 0x7fbd, 0x7fc1, 0x7fc5, 0x7fc8, 0x7fcc,
+    0x7fcf, 0x7fd2, 0x7fd5, 0x7fd7, 0x7fda, 0x7fdc, 0x7fde, 0x7fe0,
+    0x7fe2, 0x7fe4, 0x7fe6, 0x7fe7, 0x7fe9, 0x7fea, 0x7feb, 0x7fed,
+    0x7fee, 0x7fef, 0x7ff0, 0x7ff1, 0x7ff2, 0x7ff3, 0x7ff4, 0x7ff4,
+    0x000b, 0x000c, 0x000c, 0x000d, 0x000e, 0x000f, 0x0010, 0x0011,
+    0x0012, 0x0013, 0x0015, 0x0016, 0x0017, 0x0019, 0x001a, 0x001c,
+    0x001e, 0x0020, 0x0022, 0x0024, 0x0026, 0x0029, 0x002b, 0x002e,
+    0x0031, 0x0034, 0x0038, 0x003b, 0x003f, 0x0043, 0x0048, 0x004c,
+    0x0051, 0x0056, 0x005c, 0x0062, 0x0068, 0x006f, 0x0076, 0x007d,
+    0x0085, 0x008e, 0x0097, 0x00a1, 0x00ab, 0x00b6, 0x00c2, 0x00ce,
+    0x00db, 0x00e9, 0x00f8, 0x0108, 0x0119, 0x012b, 0x013e, 0x0152,
+    0x0168, 0x017f, 0x0197, 0x01b1, 0x01cd, 0x01ea, 0x0209, 0x022a,
+    0x024d, 0x0273, 0x029a, 0x02c4, 0x02f1, 0x0320, 0x0353, 0x0388,
+    0x03c1, 0x03fd, 0x043c, 0x0480, 0x04c7, 0x0513, 0x0563, 0x05b8,
+    0x0612, 0x0671, 0x06d6, 0x0740, 0x07b1, 0x0828, 0x08a5, 0x092a,
+    0x09b6, 0x0a49, 0x0ae5, 0x0b88, 0x0c34, 0x0cea, 0x0da8, 0x0e70,
+};
+
+const q7_t tanhTable_q7[256] = {
+    0x00, 0x08, 0x10, 0x18, 0x1f, 0x27, 0x2e, 0x35,
+    0x3b, 0x41, 0x47, 0x4c, 0x51, 0x56, 0x5a, 0x5e,
+    0x61, 0x65, 0x68, 0x6a, 0x6d, 0x6f, 0x71, 0x72,
+    0x74, 0x75, 0x76, 0x78, 0x78, 0x79, 0x7a, 0x7b,
+    0x7b, 0x7c, 0x7c, 0x7d, 0x7d, 0x7e, 0x7e, 0x7e,
+    0x7e, 0x7e, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x81,
+    0x81, 0x81, 0x81, 0x81, 0x81, 0x81, 0x81, 0x82,
+    0x82, 0x82, 0x82, 0x82, 0x83, 0x83, 0x84, 0x84,
+    0x85, 0x85, 0x86, 0x87, 0x88, 0x88, 0x8a, 0x8b,
+    0x8c, 0x8e, 0x8f, 0x91, 0x93, 0x96, 0x98, 0x9b,
+    0x9f, 0xa2, 0xa6, 0xaa, 0xaf, 0xb4, 0xb9, 0xbf,
+    0xc5, 0xcb, 0xd2, 0xd9, 0xe1, 0xe8, 0xf0, 0xf8,
+};
+
+const q15_t tanhTable_q15[256] = {
+    0x0000, 0x07fd, 0x0feb, 0x17b9, 0x1f59, 0x26bf, 0x2ddf, 0x34ae,
+    0x3b27, 0x4142, 0x46fd, 0x4c56, 0x514d, 0x55e2, 0x5a1a, 0x5df6,
+    0x617c, 0x64b0, 0x6797, 0x6a37, 0x6c95, 0x6eb5, 0x709e, 0x7254,
+    0x73dc, 0x753a, 0x7672, 0x7788, 0x787f, 0x795b, 0x7a1e, 0x7acb,
+    0x7b65, 0x7bee, 0x7c66, 0x7cd1, 0x7d30, 0x7d84, 0x7dce, 0x7e0f,
+    0x7e49, 0x7e7d, 0x7eaa, 0x7ed2, 0x7ef5, 0x7f14, 0x7f30, 0x7f48,
+    0x7f5e, 0x7f71, 0x7f82, 0x7f91, 0x7f9e, 0x7fa9, 0x7fb3, 0x7fbc,
+    0x7fc4, 0x7fcb, 0x7fd1, 0x7fd7, 0x7fdc, 0x7fe0, 0x7fe4, 0x7fe7,
+    0x7fea, 0x7fed, 0x7fef, 0x7ff1, 0x7ff3, 0x7ff4, 0x7ff6, 0x7ff7,
+    0x7ff8, 0x7ff9, 0x7ffa, 0x7ffa, 0x7ffb, 0x7ffc, 0x7ffc, 0x7ffd,
+    0x7ffd, 0x7ffd, 0x7ffe, 0x7ffe, 0x7ffe, 0x7ffe, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+    0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+    0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+    0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+    0x8000, 0x8000, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001,
+    0x8001, 0x8001, 0x8001, 0x8002, 0x8002, 0x8002, 0x8002, 0x8003,
+    0x8003, 0x8003, 0x8004, 0x8004, 0x8005, 0x8006, 0x8006, 0x8007,
+    0x8008, 0x8009, 0x800a, 0x800c, 0x800d, 0x800f, 0x8011, 0x8013,
+    0x8016, 0x8019, 0x801c, 0x8020, 0x8024, 0x8029, 0x802f, 0x8035,
+    0x803c, 0x8044, 0x804d, 0x8057, 0x8062, 0x806f, 0x807e, 0x808f,
+    0x80a2, 0x80b8, 0x80d0, 0x80ec, 0x810b, 0x812e, 0x8156, 0x8183,
+    0x81b7, 0x81f1, 0x8232, 0x827c, 0x82d0, 0x832f, 0x839a, 0x8412,
+    0x849b, 0x8535, 0x85e2, 0x86a5, 0x8781, 0x8878, 0x898e, 0x8ac6,
+    0x8c24, 0x8dac, 0x8f62, 0x914b, 0x936b, 0x95c9, 0x9869, 0x9b50,
+    0x9e84, 0xa20a, 0xa5e6, 0xaa1e, 0xaeb3, 0xb3aa, 0xb903, 0xbebe,
+    0xc4d9, 0xcb52, 0xd221, 0xd941, 0xe0a7, 0xe847, 0xf015, 0xf803,
+};
+
+const q15_t tanhLTable_q15[128] = {
+    0x0000, 0x0400, 0x07fd, 0x0bf7, 0x0feb, 0x13d7, 0x17b9, 0x1b90,
+    0x1f59, 0x2314, 0x26bf, 0x2a58, 0x2ddf, 0x3151, 0x34ae, 0x37f6,
+    0x3b27, 0x3e40, 0x4142, 0x442c, 0x46fd, 0x49b6, 0x4c56, 0x4edd,
+    0x514d, 0x53a3, 0x55e2, 0x580a, 0x5a1a, 0x5c13, 0x5df6, 0x5fc4,
+    0x617c, 0x6320, 0x64b0, 0x662d, 0x6797, 0x68f0, 0x6a37, 0x6b6e,
+    0x6c95, 0x6dac, 0x6eb5, 0x6fb0, 0x709e, 0x717f, 0x7254, 0x731e,
+    0x73dc, 0x7490, 0x753a, 0x75da, 0x7672, 0x7701, 0x7788, 0x7807,
+    0x787f, 0x78f0, 0x795b, 0x79bf, 0x7a1e, 0x7a77, 0x7acb, 0x7b1b,
+    0x849b, 0x84e5, 0x8535, 0x8589, 0x85e2, 0x8641, 0x86a5, 0x8710,
+    0x8781, 0x87f9, 0x8878, 0x88ff, 0x898e, 0x8a26, 0x8ac6, 0x8b70,
+    0x8c24, 0x8ce2, 0x8dac, 0x8e81, 0x8f62, 0x9050, 0x914b, 0x9254,
+    0x936b, 0x9492, 0x95c9, 0x9710, 0x9869, 0x99d3, 0x9b50, 0x9ce0,
+    0x9e84, 0xa03c, 0xa20a, 0xa3ed, 0xa5e6, 0xa7f6, 0xaa1e, 0xac5d,
+    0xaeb3, 0xb123, 0xb3aa, 0xb64a, 0xb903, 0xbbd4, 0xbebe, 0xc1c0,
+    0xc4d9, 0xc80a, 0xcb52, 0xceaf, 0xd221, 0xd5a8, 0xd941, 0xdcec,
+    0xe0a7, 0xe470, 0xe847, 0xec29, 0xf015, 0xf409, 0xf803, 0xfc00,
+};
+
+const q15_t tanhHTable_q15[192] = {
+    0x7b65, 0x7bee, 0x7c66, 0x7cd1, 0x7d30, 0x7d84, 0x7dce, 0x7e0f,
+    0x7e49, 0x7e7d, 0x7eaa, 0x7ed2, 0x7ef5, 0x7f14, 0x7f30, 0x7f48,
+    0x7f5e, 0x7f71, 0x7f82, 0x7f91, 0x7f9e, 0x7fa9, 0x7fb3, 0x7fbc,
+    0x7fc4, 0x7fcb, 0x7fd1, 0x7fd7, 0x7fdc, 0x7fe0, 0x7fe4, 0x7fe7,
+    0x7fea, 0x7fed, 0x7fef, 0x7ff1, 0x7ff3, 0x7ff4, 0x7ff6, 0x7ff7,
+    0x7ff8, 0x7ff9, 0x7ffa, 0x7ffa, 0x7ffb, 0x7ffc, 0x7ffc, 0x7ffd,
+    0x7ffd, 0x7ffd, 0x7ffe, 0x7ffe, 0x7ffe, 0x7ffe, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+    0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+    0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+    0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+    0x8000, 0x8000, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001,
+    0x8001, 0x8001, 0x8001, 0x8002, 0x8002, 0x8002, 0x8002, 0x8003,
+    0x8003, 0x8003, 0x8004, 0x8004, 0x8005, 0x8006, 0x8006, 0x8007,
+    0x8008, 0x8009, 0x800a, 0x800c, 0x800d, 0x800f, 0x8011, 0x8013,
+    0x8016, 0x8019, 0x801c, 0x8020, 0x8024, 0x8029, 0x802f, 0x8035,
+    0x803c, 0x8044, 0x804d, 0x8057, 0x8062, 0x806f, 0x807e, 0x808f,
+    0x80a2, 0x80b8, 0x80d0, 0x80ec, 0x810b, 0x812e, 0x8156, 0x8183,
+    0x81b7, 0x81f1, 0x8232, 0x827c, 0x82d0, 0x832f, 0x839a, 0x8412,
+};

Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_no_shift.c

@@ -0,0 +1,134 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_q7_to_q15_no_shift.c
+ * Description:  Converts the elements of the Q7 vector to Q15 vector without left-shift
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnsupportfunctions.h"
+
+/**    
+ * @ingroup groupSupport    
+ */
+
+/**    
+ * @addtogroup nndata_convert    
+ * @{    
+ */
+
+/**    
+ * @brief Converts the elements of the Q7 vector to Q15 vector without left-shift 
+ * @param[in]       *pSrc points to the Q7 input vector    
+ * @param[out]      *pDst points to the Q15 output vector   
+ * @param[in]       blockSize length of the input vector    
+ * @return none.    
+ *    
+ * \par Description:    
+ *    
+ * The equation used for the conversion process is:    
+ *   
+ * <pre>    
+ * 	pDst[n] = (q15_t) pSrc[n];   0 <= n < blockSize.    
+ * </pre>    
+ *   
+ */
+
+void arm_q7_to_q15_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize)
+{
+    const q7_t *pIn = pSrc;     /* Src pointer */
+    uint32_t  blkCnt;           /* loop counter */
+
+#ifndef ARM_MATH_CM0_FAMILY
+    q31_t     in;
+    q31_t     in1, in2;
+    q31_t     out1, out2;
+
+    /* Run the below code for Cortex-M4 and Cortex-M3 */
+
+    /*loop Unrolling */
+    blkCnt = blockSize >> 2u;
+
+    /* First part of the processing with loop unrolling.  Compute 4 outputs at a time.    
+     ** a second loop below computes the remaining 1 to 3 samples. */
+    while (blkCnt > 0u)
+    {
+        /* C = (q15_t) A << 8 */
+        /* convert from q7 to q15 and then store the results in the destination buffer */
+        in = *__SIMD32(pIn)++;
+
+        /* rotatate in by 8 and extend two q7_t values to q15_t values */
+        in1 = __SXTB16(__ROR(in, 8));
+
+        /* extend remainig two q7_t values to q15_t values */
+        in2 = __SXTB16(in);
+
+#ifndef ARM_MATH_BIG_ENDIAN
+
+        out2 = __PKHTB(in1, in2, 16);
+        out1 = __PKHBT(in2, in1, 16);
+
+#else
+
+        out1 = __PKHTB(in1, in2, 16);
+        out2 = __PKHBT(in2, in1, 16);
+
+#endif
+
+        *__SIMD32(pDst)++ = out1;
+        *__SIMD32(pDst)++ = out2;
+
+        /* Decrement the loop counter */
+        blkCnt--;
+    }
+
+    /* If the blockSize is not a multiple of 4, compute any remaining output samples here.    
+     ** No loop unrolling is used. */
+    blkCnt = blockSize % 0x4u;
+
+#else
+
+    /* Run the below code for Cortex-M0 */
+
+    /* Loop over blockSize number of values */
+    blkCnt = blockSize;
+
+#endif                          /* #ifndef ARM_MATH_CM0_FAMILY */
+
+    while (blkCnt > 0u)
+    {
+        /* C = (q15_t) A << 8 */
+        /* convert from q7 to q15 and then store the results in the destination buffer */
+        *pDst++ = (q15_t) * pIn++;
+
+        /* Decrement the loop counter */
+        blkCnt--;
+    }
+
+}
+
+/**    
+ * @} end of nndata_convert group   
+ */

Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_reordered_no_shift.c

@@ -0,0 +1,145 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_q7_to_q15_reordered_no_shift.c
+ * Description:  Converts the elements of the Q7 vector to reordered Q15 vector without left-shift
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnsupportfunctions.h"
+
+/**    
+ * @ingroup groupSupport    
+ */
+
+/**    
+ * @addtogroup nndata_convert    
+ * @{    
+ */
+
+/**    
+ * @brief Converts the elements of the Q7 vector to reordered Q15 vector without left-shift
+ * @param[in]       *pSrc points to the Q7 input vector    
+ * @param[out]      *pDst points to the Q15 output vector   
+ * @param[in]       blockSize length of the input vector    
+ * @return none.    
+ *    
+ * @details
+ *
+ * This function does the q7 to q15 expansion with re-ordering 
+ *
+ * <pre>
+ *                          |   A1   |   A2   |   A3   |   A4   |
+ *
+ *                           0      7 8     15 16    23 24    31
+ * </pre>
+ *
+ * is converted into:
+ *
+ * <pre>
+ *  |       A1       |       A3       |   and  |       A2       |       A4       |
+ *
+ *   0             15 16            31          0             15 16            31
+ * </pre>
+ *
+ *
+ * This looks strange but is natural considering how sign-extension is done at
+ * assembly level. 
+ *
+ * The expansion of other other oprand will follow the same rule so that the end 
+ * results are the same.
+ *
+ * The tail (i.e., last (N % 4) elements) will still be in original order.
+ *   
+ */
+
+void arm_q7_to_q15_reordered_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize)
+{
+    const q7_t *pIn = pSrc;     /* Src pointer */
+    uint32_t  blkCnt;           /* loop counter */
+
+#ifndef ARM_MATH_CM0_FAMILY
+    q31_t     in;
+    q31_t     in1, in2;
+
+    /* Run the below code for Cortex-M4 and Cortex-M3 */
+
+    /*loop Unrolling */
+    blkCnt = blockSize >> 2u;
+
+    /* First part of the processing with loop unrolling.  Compute 4 outputs at a time.    
+     ** a second loop below computes the remaining 1 to 3 samples. */
+    while (blkCnt > 0u)
+    {
+        /* C = (q15_t) A << 8 */
+        /* convert from q7 to q15 and then store the results in the destination buffer */
+        in = *__SIMD32(pIn)++;
+
+        /* rotatate in by 8 and extend two q7_t values to q15_t values */
+        in1 = __SXTB16(__ROR(in, 8));
+
+        /* extend remainig two q7_t values to q15_t values */
+        in2 = __SXTB16(in);
+
+#ifndef ARM_MATH_BIG_ENDIAN
+        *__SIMD32(pDst)++ = in2;
+        *__SIMD32(pDst)++ = in1;
+#else
+        *__SIMD32(pDst)++ = in1;
+        *__SIMD32(pDst)++ = in2;
+#endif
+
+        /* Decrement the loop counter */
+        blkCnt--;
+    }
+
+    /* If the blockSize is not a multiple of 4, compute any remaining output samples here.    
+     ** No loop unrolling is used. */
+    blkCnt = blockSize % 0x4u;
+
+#else
+
+    /* Run the below code for Cortex-M0 */
+
+    /* Loop over blockSize number of values */
+    blkCnt = blockSize;
+
+#endif                          /* #ifndef ARM_MATH_CM0_FAMILY */
+
+    while (blkCnt > 0u)
+    {
+        /* C = (q15_t) A << 8 */
+        /* convert from q7 to q15 and then store the results in the destination buffer */
+        *pDst++ = (q15_t) * pIn++;
+
+        /* Decrement the loop counter */
+        blkCnt--;
+    }
+
+}
+
+/**    
+ * @} end of q7_to_x group    
+ */

Drivers/CMSIS/NN/Source/PoolingFunctions/arm_pool_q7_HWC.c

@@ -0,0 +1,448 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_pool_q7_HWC.c
+ * Description:  Pooling function implementations
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+#if defined (ARM_MATH_DSP)
+
+/**
+ * @brief A few utility functions used by pooling functions
+ *
+ * 
+ */
+
+static void buffer_scale_back_q15_to_q7(q15_t * buffer, q7_t * target, uint16_t length, uint16_t scale)
+{
+    int       i;
+
+    for (i = 0; i < length; i++)
+    {
+        target[i] = (q7_t) (buffer[i] / scale);
+    }
+}
+
+static void compare_and_replace_if_larger_q7(q7_t * base,   // base data
+                                             q7_t * target, // compare target
+                                             const uint16_t length  // data length
+    )
+{
+    q7_t     *pIn = base;
+    q7_t     *pCom = target;
+    union arm_nnword in;
+    union arm_nnword com;
+    uint16_t  cnt = length >> 2;
+
+    while (cnt > 0u)
+    {
+        in.word = *__SIMD32(pIn);
+        com.word = *__SIMD32(pCom)++;
+
+        // if version
+        if (com.bytes[0] > in.bytes[0])
+            in.bytes[0] = com.bytes[0];
+        if (com.bytes[1] > in.bytes[1])
+            in.bytes[1] = com.bytes[1];
+        if (com.bytes[2] > in.bytes[2])
+            in.bytes[2] = com.bytes[2];
+        if (com.bytes[3] > in.bytes[3])
+            in.bytes[3] = com.bytes[3];
+
+        *__SIMD32(pIn)++ = in.word;
+
+        cnt--;
+    }
+}
+
+static void accumulate_q7_to_q15(q15_t * base, q7_t * target, const uint16_t length)
+{
+    q15_t    *pCnt = base;
+    q7_t     *pV = target;
+    q31_t     v1, v2, vo1, vo2;
+    uint16_t  cnt = length >> 2;
+    q31_t     in;
+
+    while (cnt > 0u)
+    {
+        q31_t     value = *__SIMD32(pV)++;
+        v1 = __SXTB16(__ROR(value, 8));
+        v2 = __SXTB16(value);
+#ifndef ARM_MATH_BIG_ENDIAN
+
+        vo2 = __PKHTB(v1, v2, 16);
+        vo1 = __PKHBT(v2, v1, 16);
+
+#else
+
+        vo1 = __PKHTB(v1, v2, 16);
+        vo2 = __PKHBT(v2, v1, 16);
+
+#endif
+
+        in = *__SIMD32(pCnt);
+        *__SIMD32(pCnt)++ = __QADD16(vo1, in);
+
+        in = *__SIMD32(pCnt);
+        *__SIMD32(pCnt)++ = __QADD16(vo2, in);
+
+        cnt--;
+    }
+    cnt = length & 0x3;
+    while (cnt > 0u)
+    {
+        *pCnt++ += *pV++;
+        cnt--;
+    }
+}
+
+#endif                          // ARM_MATH_DSP
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Pooling
+ * @{
+ */
+
+  /**
+   * @brief Q7 max pooling function
+   * @param[in, out]  Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @return none.
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * bufferA size:  0
+   *
+   * The pooling function is implemented as split x-pooling then
+   * y-pooling.
+   *
+   * This pooling function is input-destructive. Input data is undefined
+   * after calling this function.
+   *
+   */
+
+void
+arm_maxpool_q7_HWC(q7_t * Im_in,
+                   const uint16_t dim_im_in,
+                   const uint16_t ch_im_in,
+                   const uint16_t dim_kernel,
+                   const uint16_t padding,
+                   const uint16_t stride, const uint16_t dim_im_out, q7_t * bufferA, q7_t * Im_out)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    int16_t   i_x, i_y;
+
+    /* first does the pooling along x axis */
+    for (i_y = 0; i_y < dim_im_in; i_y++)
+    {
+
+        for (i_x = 0; i_x < dim_im_out; i_x++)
+        {
+            /* for each output pixel */
+            q7_t     *target = Im_in + (i_y * dim_im_in + i_x) * ch_im_in;
+            q7_t     *win_start;
+            q7_t     *win_stop;
+            if (i_x * stride - padding < 0)
+            {
+                win_start = target;
+            } else
+            {
+                win_start = Im_in + (i_y * dim_im_in + i_x * stride - padding) * ch_im_in;
+            }
+
+            if (i_x * stride - padding + dim_kernel >= dim_im_in)
+            {
+                win_stop = Im_in + (i_y * dim_im_in + dim_im_in) * ch_im_in;
+            } else
+            {
+                win_stop = Im_in + (i_y * dim_im_in + i_x * stride - padding + dim_kernel) * ch_im_in;
+            }
+
+            /* first step is to copy over initial data */
+            /* arm_copy_q7(win_start, target, ch_im_in); */
+            memmove(target, win_start, ch_im_in);
+
+            /* start the max operation from the second part */
+            win_start += ch_im_in;
+            for (; win_start < win_stop; win_start += ch_im_in)
+            {
+                compare_and_replace_if_larger_q7(target, win_start, ch_im_in);
+            }
+        }
+    }
+
+    /* then does the pooling along y axis */
+    for (i_y = 0; i_y < dim_im_out; i_y++)
+    {
+
+        /* for each output row */
+        q7_t     *target = Im_out + i_y * dim_im_out * ch_im_in;
+        q7_t     *row_start;
+        q7_t     *row_end;
+        /* setting the starting row */
+        if (i_y * stride - padding < 0)
+        {
+            row_start = Im_in;
+        } else
+        {
+            row_start = Im_in + (i_y * stride - padding) * dim_im_in * ch_im_in;
+        }
+        /* setting the stopping row */
+        if (i_y * stride - padding + dim_kernel >= dim_im_in)
+        {
+            row_end = Im_in + dim_im_in * dim_im_in * ch_im_in;
+        } else
+        {
+            row_end = Im_in + (i_y * stride - padding + dim_kernel) * dim_im_in * ch_im_in;
+        }
+
+        /* copy over the first row */
+        /* arm_copy_q7(row_start, target, dim_im_out * ch_im_in); */
+        memmove(target, row_start, dim_im_out * ch_im_in);
+
+        /* move over to next row */
+        row_start += ch_im_in * dim_im_in;
+
+        for (; row_start < row_end; row_start += dim_im_in * ch_im_in)
+        {
+            compare_and_replace_if_larger_q7(target, row_start, dim_im_out * ch_im_in);
+        }
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+    int16_t   i_ch_in, i_x, i_y;
+    int16_t   k_x, k_y;
+
+    for (i_ch_in = 0; i_ch_in < ch_im_in; i_ch_in++)
+    {
+        for (i_y = 0; i_y < dim_im_out; i_y++)
+        {
+            for (i_x = 0; i_x < dim_im_out; i_x++)
+            {
+                int       max = -129;
+                for (k_y = i_y * stride - padding; k_y < i_y * stride - padding + dim_kernel; k_y++)
+                {
+                    for (k_x = i_x * stride - padding; k_x < i_x * stride - padding + dim_kernel; k_x++)
+                    {
+                        if (k_y >= 0 && k_x >= 0 && k_y < dim_im_in && k_x < dim_im_in)
+                        {
+                            if (Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)] > max)
+                            {
+                                max = Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)];
+                            }
+                        }
+                    }
+                }
+                Im_out[i_ch_in + ch_im_in * (i_x + i_y * dim_im_out)] = max;
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+}
+
+  /**
+   * @brief Q7 average pooling function
+   * @param[in,out]   Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @return none.
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * bufferA size:  2*dim_im_out*ch_im_in
+   *
+   * The pooling function is implemented as split x-pooling then
+   * y-pooling.
+   *
+   * This pooling function is input-destructive. Input data is undefined
+   * after calling this function.
+   *
+   */
+
+void
+arm_avepool_q7_HWC(q7_t * Im_in,
+                   const uint16_t dim_im_in,
+                   const uint16_t ch_im_in,
+                   const uint16_t dim_kernel,
+                   const uint16_t padding,
+                   const uint16_t stride, const uint16_t dim_im_out, q7_t * bufferA, q7_t * Im_out)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    q15_t    *buffer = (q15_t *) bufferA;
+    int16_t   i_x, i_y;
+    int16_t   count = 0;
+
+    /* first does the pooling along x axis */
+    for (i_y = 0; i_y < dim_im_in; i_y++)
+    {
+
+        for (i_x = 0; i_x < dim_im_out; i_x++)
+        {
+            /* for each output pixel */
+            q7_t     *target = Im_in + (i_y * dim_im_in + i_x) * ch_im_in;
+            q7_t     *win_start;
+            q7_t     *win_stop;
+            if (i_x * stride - padding < 0)
+            {
+                win_start = target;
+            } else
+            {
+                win_start = Im_in + (i_y * dim_im_in + i_x * stride - padding) * ch_im_in;
+            }
+
+            if (i_x * stride - padding + dim_kernel >= dim_im_in)
+            {
+                win_stop = Im_in + (i_y * dim_im_in + dim_im_in) * ch_im_in;
+            } else
+            {
+                win_stop = Im_in + (i_y * dim_im_in + i_x * stride - padding + dim_kernel) * ch_im_in;
+            }
+
+            /* first step is to copy over initial data */
+            arm_q7_to_q15_no_shift(win_start, buffer, ch_im_in);
+            count = 1;
+
+            /* start the max operation from the second part */
+            win_start += ch_im_in;
+            for (; win_start < win_stop; win_start += ch_im_in)
+            {
+                accumulate_q7_to_q15(buffer, win_start, ch_im_in);
+                count++;
+            }
+            buffer_scale_back_q15_to_q7(buffer, target, ch_im_in, count);
+        }
+    }
+
+    /* then does the pooling along y axis */
+    for (i_y = 0; i_y < dim_im_out; i_y++)
+    {
+        /* for each output row */
+        q7_t     *target = Im_out + i_y * dim_im_out * ch_im_in;
+        q7_t     *row_start;
+        q7_t     *row_end;
+        /* setting the starting row */
+        if (i_y * stride - padding < 0)
+        {
+            row_start = Im_in;
+        } else
+        {
+            row_start = Im_in + (i_y * stride - padding) * dim_im_in * ch_im_in;
+        }
+        /* setting the stopping row */
+        if (i_y * stride - padding + dim_kernel >= dim_im_in)
+        {
+            row_end = Im_in + dim_im_in * dim_im_in * ch_im_in;
+        } else
+        {
+            row_end = Im_in + (i_y * stride - padding + dim_kernel) * dim_im_in * ch_im_in;
+        }
+
+        /* copy over the first row */
+        arm_q7_to_q15_no_shift(row_start, buffer, dim_im_out * ch_im_in);
+        count = 1;
+
+        /* move over to next row */
+        row_start += ch_im_in * dim_im_in;
+
+        for (; row_start < row_end; row_start += dim_im_in * ch_im_in)
+        {
+            accumulate_q7_to_q15(buffer, row_start, dim_im_out * ch_im_in);
+            count++;
+        }
+        buffer_scale_back_q15_to_q7(buffer, target, dim_im_out * ch_im_in, count);
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+    int16_t   i_ch_in, i_x, i_y;
+    int16_t   k_x, k_y;
+
+    for (i_ch_in = 0; i_ch_in < ch_im_in; i_ch_in++)
+    {
+        for (i_y = 0; i_y < dim_im_out; i_y++)
+        {
+            for (i_x = 0; i_x < dim_im_out; i_x++)
+            {
+                int       sum = 0;
+                int       count = 0;
+                for (k_y = i_y * stride - padding; k_y < i_y * stride - padding + dim_kernel; k_y++)
+                {
+                    for (k_x = i_x * stride - padding; k_x < i_x * stride - padding + dim_kernel; k_x++)
+                    {
+                        if (k_y >= 0 && k_x >= 0 && k_y < dim_im_in && k_x < dim_im_in)
+                        {
+                            sum += Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)];
+                            count++;
+                        }
+                    }
+                }
+                Im_out[i_ch_in + ch_im_in * (i_x + i_y * dim_im_out)] = sum / count;
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+}
+
+/**
+ * @} end of Pooling group
+ */

Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q15.c

@@ -0,0 +1,120 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_softmax_q15.c
+ * Description:  Q15 softmax function
+ *
+ * $Date:        20. February 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Softmax
+ * @{
+ */
+
+  /**
+   * @brief Q15 softmax function
+   * @param[in]       vec_in      pointer to input vector
+   * @param[in]       dim_vec     input vector dimention
+   * @param[out]      p_out       pointer to output vector
+   * @return none.
+   *
+   * @details
+   *
+   *  Here, instead of typical e based softmax, we use
+   *  2-based softmax, i.e.,:
+   *
+   *  y_i = 2^(x_i) / sum(2^x_j)
+   *
+   *  The relative output will be different here.
+   *  But mathematically, the gradient will be the same
+   *  with a log(2) scaling factor.
+   *
+   */
+
+void arm_softmax_q15(const q15_t * vec_in, const uint16_t dim_vec, q15_t * p_out)
+{
+    q31_t     sum;
+    int16_t   i;
+    uint8_t   shift;
+    q31_t     base;
+    base = -1 * 0x100000;
+    for (i = 0; i < dim_vec; i++)
+    {
+        if (vec_in[i] > base)
+        {
+            base = vec_in[i];
+        }
+    }
+
+    /* we ignore really small values  
+     * anyway, they will be 0 after shrinking
+     * to q15_t
+     */
+    base = base - 16;
+
+    sum = 0;
+
+    for (i = 0; i < dim_vec; i++)
+    {
+        if (vec_in[i] > base)
+        {
+            shift = (uint8_t)__USAT(vec_in[i] - base, 5);
+            sum += 0x1 << shift;
+        }
+    }
+
+    /* This is effectively (0x1 << 32) / sum */
+    int64_t div_base = 0x100000000LL;
+    int output_base = (int32_t)(div_base / sum);
+
+    /* Final confidence will be output_base >> ( 17 - (vec_in[i] - base) )
+     * so 32768 (0x1<<15) -> 100% confidence when sum = 0x1 << 16, output_base = 0x1 << 16
+     * and vec_in[i]-base = 16
+     */
+    for (i = 0; i < dim_vec; i++)
+    {
+        if (vec_in[i] > base) 
+        {
+            /* Here minimum value of 17+base-vec[i] will be 1 */
+            shift = (uint8_t)__USAT(17+base-vec_in[i], 5);
+            p_out[i] = (q15_t) __SSAT((output_base >> shift), 16);
+        } else
+        {
+            p_out[i] = 0;
+        }
+    }
+
+}
+
+/**
+ * @} end of Softmax group
+ */

Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c

@@ -0,0 +1,121 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_softmax_q7.c
+ * Description:  Q7 softmax function
+ *
+ * $Date:        20. February 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Softmax
+ * @{
+ */
+
+  /**
+   * @brief Q7 softmax function
+   * @param[in]       vec_in      pointer to input vector
+   * @param[in]       dim_vec     input vector dimention
+   * @param[out]      p_out       pointer to output vector
+   * @return none.
+   *
+   * @details
+   *
+   *  Here, instead of typical natural logarithm e based softmax, we use
+   *  2-based softmax here, i.e.,:
+   * 
+   *  y_i = 2^(x_i) / sum(2^x_j)
+   *
+   *  The relative output will be different here.
+   *  But mathematically, the gradient will be the same
+   *  with a log(2) scaling factor.
+   *
+   */
+
+void arm_softmax_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out)
+{
+    q31_t     sum;
+    int16_t   i;
+    uint8_t   shift;
+    q15_t     base;
+    base = -257;
+
+    /* We first search for the maximum */
+    for (i = 0; i < dim_vec; i++)
+    {
+        if (vec_in[i] > base)
+        {
+            base = vec_in[i];
+        }
+    }
+
+    /* 
+     * So the base is set to max-8, meaning 
+     * that we ignore really small values. 
+     * anyway, they will be 0 after shrinking to q7_t.
+     */
+    base = base - 8;
+
+    sum = 0;
+
+    for (i = 0; i < dim_vec; i++)
+    {
+        if (vec_in[i] > base) 
+        {
+            shift = (uint8_t)__USAT(vec_in[i] - base, 5);
+            sum += 0x1 << shift;
+        }
+    }
+
+    /* This is effectively (0x1 << 20) / sum */
+    int output_base = 0x100000 / sum;
+
+    /* 
+     * Final confidence will be output_base >> ( 13 - (vec_in[i] - base) )
+     * so 128 (0x1<<7) -> 100% confidence when sum = 0x1 << 8, output_base = 0x1 << 12 
+     * and vec_in[i]-base = 8
+     */
+    for (i = 0; i < dim_vec; i++) 
+    {
+        if (vec_in[i] > base) 
+        {
+            /* Here minimum value of 13+base-vec_in[i] will be 5 */
+            shift = (uint8_t)__USAT(13+base-vec_in[i], 5);
+            p_out[i] = (q7_t) __SSAT((output_base >> shift), 8);
+        } else {
+            p_out[i] = 0;
+        }
+    }
+}
+
+/**
+ * @} end of Softmax group
+ */