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Drivers/CMSIS/NN/Examples/IAR/iar_nn_examples/NN-example-gru/arm_nnexamples_gru.cpp

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+/* ----------------------------------------------------------------------
+* 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/IAR/iar_nn_examples/NN-example-gru/readme_iar.txt

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+CMSIS NN Lib example arm_nnexample_gru0 for
+  Cortex-M0, Cortex-M3, Cortex-M4 and Cortex-M7.
+
+The example is configured for IAR Embedded Workbench for ARM Simulator.
+
+When changing target, remember to change the ARM_MATH_CMx and __FPU_PRESENT
+Preprocessor defines for C/C++ Compiler