1011 lines
48 KiB
C
1011 lines
48 KiB
C
/*
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* Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
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*
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* SPDX-License-Identifier: Apache-2.0
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*
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* Licensed under the Apache License, Version 2.0 (the License); you may
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* not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an AS IS BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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/* ----------------------------------------------------------------------
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* Project: CMSIS NN Library
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* Title: arm_nnfunctions.h
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* Description: Public header file for CMSIS NN Library
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*
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* $Date: 13. July 2018
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* $Revision: V.1.0.0
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*
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* Target Processor: Cortex-M cores
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* -------------------------------------------------------------------- */
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/**
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\mainpage CMSIS NN Software Library
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*
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* Introduction
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* ------------
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*
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* This user manual describes the CMSIS NN software library,
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* a collection of efficient neural network kernels developed to maximize the
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* performance and minimize the memory footprint of neural networks on Cortex-M processor cores.
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*
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* The library is divided into a number of functions each covering a specific category:
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* - Neural Network Convolution Functions
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* - Neural Network Activation Functions
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* - Fully-connected Layer Functions
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* - Neural Network Pooling Functions
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* - Softmax Functions
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* - Neural Network Support Functions
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*
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* The library has separate functions for operating on different weight and activation data
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* types including 8-bit integers (q7_t) and 16-bit integers (q15_t). The descrition of the
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* kernels are included in the function description. The implementation details are also
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* described in this paper [1].
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*
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* Block Diagram
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* --------
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* \image html CMSIS-NN-OVERVIEW.PNG
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*
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* Examples
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* --------
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*
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* The library ships with a number of examples which demonstrate how to use the library functions.
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*
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* Pre-processor Macros
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* ------------
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*
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* Each library project have differant pre-processor macros.
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*
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* - ARM_MATH_DSP:
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*
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* Define macro ARM_MATH_DSP, If the silicon supports DSP instructions.
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*
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* - ARM_MATH_BIG_ENDIAN:
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*
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* Define macro ARM_MATH_BIG_ENDIAN to build the library for big endian targets. By default library builds for little endian targets.
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*
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* - ARM_NN_TRUNCATE:
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*
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* Define macro ARM_NN_TRUNCATE to use floor instead of round-to-the-nearest-int for the computation.
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*
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* Copyright Notice
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* ------------
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*
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* Copyright (C) 2010-2018 Arm Limited. All rights reserved.
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*
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* [1] CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs https://arxiv.org/abs/1801.06601
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*/
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/**
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* @defgroup groupNN Neural Network Functions
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* These functions perform basic operations for neural network layers.
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*/
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#ifndef _ARM_NNFUNCTIONS_H
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#define _ARM_NNFUNCTIONS_H
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#include "arm_nnsupportfunctions.h"
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#include "arm_nn_tables.h"
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#define USE_INTRINSIC
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//#define ARM_NN_TRUNCATE /* This config the rounding model to floor or round to the nearest int */
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#ifdef __cplusplus
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extern "C"
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{
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#endif
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/**
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* @defgroup NNConv Neural Network Convolution Functions
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*
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* Perform convolution layer
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*
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* The convolution is implemented in 2 steps: im2col and GEMM
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*
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* im2col is a process of converting each patch of image data into
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* a column. After im2col, the convolution is computed as matrix-matrix
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* multiplication.
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*
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* To reduce the memory footprint, the im2col is performed partially.
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* Each iteration, only a few column (i.e., patches) are generated and
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* computed with GEMM kernels similar to CMSIS-DSP arm_mat_mult functions.
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*
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*/
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/**
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* @brief Basic Q7 convolution function
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* @param[in] Im_in pointer to input tensor
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* @param[in] dim_im_in input tensor dimention
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* @param[in] ch_im_in number of input tensor channels
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* @param[in] wt pointer to kernel weights
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* @param[in] ch_im_out number of filters, i.e., output tensor channels
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* @param[in] dim_kernel filter kernel size
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* @param[in] padding padding sizes
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* @param[in] stride convolution stride
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* @param[in] bias pointer to bias
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* @param[in] bias_shift amount of left-shift for bias
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* @param[in] out_shift amount of right-shift for output
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* @param[in,out] Im_out pointer to output tensor
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* @param[in] dim_im_out output tensor dimension
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* @param[in,out] bufferA pointer to buffer space for input
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* @param[in,out] bufferB pointer to buffer space for output
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* @return The function returns <code>ARM_MATH_SUCCESS</code>
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*
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*/
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arm_status arm_convolve_HWC_q7_basic(const q7_t * Im_in,
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const uint16_t dim_im_in,
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const uint16_t ch_im_in,
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const q7_t * wt,
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const uint16_t ch_im_out,
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const uint16_t dim_kernel,
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const uint16_t padding,
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const uint16_t stride,
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const q7_t * bias,
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const uint16_t bias_shift,
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const uint16_t out_shift,
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q7_t * Im_out,
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const uint16_t dim_im_out,
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q15_t * bufferA,
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q7_t * bufferB);
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/**
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* @brief Basic Q7 convolution function (non-sqaure shape)
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* @param[in] Im_in pointer to input tensor
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* @param[in] dim_im_in_x input tensor dimention x
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* @param[in] dim_im_in_y input tensor dimention y
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* @param[in] ch_im_in number of input tensor channels
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* @param[in] wt pointer to kernel weights
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* @param[in] ch_im_out number of filters, i.e., output tensor channels
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* @param[in] dim_kernel_x filter kernel size x
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* @param[in] dim_kernel_y filter kernel size y
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* @param[in] padding_x padding size x
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* @param[in] padding_y padding size y
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* @param[in] stride_x convolution stride x
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* @param[in] stride_y convolution stride y
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* @param[in] bias pointer to bias
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* @param[in] bias_shift amount of left-shift for bias
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* @param[in] out_shift amount of right-shift for output
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* @param[in,out] Im_out pointer to output tensor
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* @param[in] dim_im_out_x output tensor dimension x
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* @param[in] dim_im_out_y output tensor dimension y
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* @param[in,out] bufferA pointer to buffer space for input
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* @param[in,out] bufferB pointer to buffer space for output
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* @return The function returns <code>ARM_MATH_SUCCESS</code>
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*/
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arm_status arm_convolve_HWC_q7_basic_nonsquare(const q7_t * Im_in,
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const uint16_t dim_im_in_x,
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const uint16_t dim_im_in_y,
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const uint16_t ch_im_in,
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const q7_t * wt,
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const uint16_t ch_im_out,
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const uint16_t dim_kernel_x,
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const uint16_t dim_kernel_y,
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const uint16_t padding_x,
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const uint16_t padding_y,
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const uint16_t stride_x,
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const uint16_t stride_y,
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const q7_t * bias,
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const uint16_t bias_shift,
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const uint16_t out_shift,
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q7_t * Im_out,
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const uint16_t dim_im_out_x,
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const uint16_t dim_im_out_y,
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q15_t * bufferA,
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q7_t * bufferB);
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/**
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* @brief Basic Q15 convolution function
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* @param[in] Im_in pointer to input tensor
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* @param[in] dim_im_in input tensor dimention
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* @param[in] ch_im_in number of input tensor channels
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* @param[in] wt pointer to kernel weights
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* @param[in] ch_im_out number of filters, i.e., output tensor channels
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* @param[in] dim_kernel filter kernel size
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* @param[in] padding padding sizes
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* @param[in] stride convolution stride
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* @param[in] bias pointer to bias
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* @param[in] bias_shift amount of left-shift for bias
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* @param[in] out_shift amount of right-shift for output
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* @param[in,out] Im_out pointer to output tensor
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* @param[in] dim_im_out output tensor dimension
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* @param[in,out] bufferA pointer to buffer space for input
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* @param[in,out] bufferB pointer to buffer space for output
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* @return The function returns <code>ARM_MATH_SUCCESS</code>
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*
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*/
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arm_status arm_convolve_HWC_q15_basic(const q15_t * Im_in,
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const uint16_t dim_im_in,
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const uint16_t ch_im_in,
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const q15_t * wt,
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const uint16_t ch_im_out,
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const uint16_t dim_kernel,
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const uint16_t padding,
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const uint16_t stride,
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const q15_t * bias,
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const uint16_t bias_shift,
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const uint16_t out_shift,
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q15_t * Im_out,
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const uint16_t dim_im_out,
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q15_t * bufferA,
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q7_t * bufferB);
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/**
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* @brief Fast Q7 convolution function
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* @param[in] Im_in pointer to input tensor
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* @param[in] dim_im_in input tensor dimention
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* @param[in] ch_im_in number of input tensor channels
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* @param[in] wt pointer to kernel weights
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* @param[in] ch_im_out number of filters, i.e., output tensor channels
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* @param[in] dim_kernel filter kernel size
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* @param[in] padding padding sizes
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* @param[in] stride convolution stride
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* @param[in] bias pointer to bias
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* @param[in] bias_shift amount of left-shift for bias
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* @param[in] out_shift amount of right-shift for output
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* @param[in,out] Im_out pointer to output tensor
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* @param[in] dim_im_out output tensor dimension
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* @param[in,out] bufferA pointer to buffer space for input
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* @param[in,out] bufferB pointer to buffer space for output
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* @return The function returns either
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* <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
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*
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* This function is the version with full list of optimization tricks, but with
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* some contraints:
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* ch_im_in is multiple of 4
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* ch_im_out is multiple of 2
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*/
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arm_status arm_convolve_HWC_q7_fast(const q7_t * Im_in,
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const uint16_t dim_im_in,
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const uint16_t ch_im_in,
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const q7_t * wt,
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const uint16_t ch_im_out,
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const uint16_t dim_kernel,
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const uint16_t padding,
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const uint16_t stride,
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const q7_t * bias,
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const uint16_t bias_shift,
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const uint16_t out_shift,
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q7_t * Im_out,
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const uint16_t dim_im_out,
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q15_t * bufferA,
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q7_t * bufferB);
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/**
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* @brief Fast Q7 convolution function (non-sqaure shape)
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* @param[in] Im_in pointer to input tensor
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* @param[in] dim_im_in_x input tensor dimention x
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* @param[in] dim_im_in_y input tensor dimention y
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* @param[in] ch_im_in number of input tensor channels
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* @param[in] wt pointer to kernel weights
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* @param[in] ch_im_out number of filters, i.e., output tensor channels
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* @param[in] dim_kernel_x filter kernel size x
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* @param[in] dim_kernel_y filter kernel size y
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* @param[in] padding_x padding size x
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* @param[in] padding_y padding size y
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* @param[in] stride_x convolution stride x
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* @param[in] stride_y convolution stride y
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* @param[in] bias pointer to bias
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* @param[in] bias_shift amount of left-shift for bias
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* @param[in] out_shift amount of right-shift for output
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* @param[in,out] Im_out pointer to output tensor
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* @param[in] dim_im_out_x output tensor dimension x
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* @param[in] dim_im_out_y output tensor dimension y
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* @param[in,out] bufferA pointer to buffer space for input
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* @param[in,out] bufferB pointer to buffer space for output
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* @return The function returns either
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* <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
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*
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* This function is the version with full list of optimization tricks, but with
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* some contraints:
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* ch_im_in is multiple of 4
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* ch_im_out is multiple of 2
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*/
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arm_status arm_convolve_HWC_q7_fast_nonsquare(const q7_t * Im_in,
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const uint16_t dim_im_in_x,
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const uint16_t dim_im_in_y,
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const uint16_t ch_im_in,
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const q7_t * wt,
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const uint16_t ch_im_out,
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const uint16_t dim_kernel_x,
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const uint16_t dim_kernel_y,
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const uint16_t padding_x,
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const uint16_t padding_y,
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const uint16_t stride_x,
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const uint16_t stride_y,
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const q7_t * bias,
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const uint16_t bias_shift,
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const uint16_t out_shift,
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q7_t * Im_out,
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const uint16_t dim_im_out_x,
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const uint16_t dim_im_out_y,
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q15_t * bufferA,
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q7_t * bufferB);
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/**
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* @brief Fast Q7 version of 1x1 convolution (non-sqaure shape)
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* @param[in] Im_in pointer to input tensor
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* @param[in] dim_im_in_x input tensor dimention x
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* @param[in] dim_im_in_y input tensor dimention y
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* @param[in] ch_im_in number of input tensor channels
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* @param[in] wt pointer to kernel weights
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* @param[in] ch_im_out number of filters, i.e., output tensor channels
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* @param[in] dim_kernel_x filter kernel size x
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* @param[in] dim_kernel_y filter kernel size y
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* @param[in] padding_x padding size x
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* @param[in] padding_y padding size y
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* @param[in] stride_x convolution stride x
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* @param[in] stride_y convolution stride y
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* @param[in] bias pointer to bias
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* @param[in] bias_shift amount of left-shift for bias
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* @param[in] out_shift amount of right-shift for output
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* @param[in,out] Im_out pointer to output tensor
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* @param[in] dim_im_out_x output tensor dimension x
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* @param[in] dim_im_out_y output tensor dimension y
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* @param[in,out] bufferA pointer to buffer space for input
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* @param[in,out] bufferB pointer to buffer space for output
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* @return The function returns either
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* <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
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*
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* This function implement convolution with 1x1 kernel size (i.e., dim_kernel_x=1
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* and dim_kernel_y=1). It can be used for
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* second half of MobileNets after depthwise separable convolution.
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*
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* This function is the version with full list of optimization tricks, but with
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* some contraints:
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* ch_im_in is multiple of 4
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* ch_im_out is multiple of 2
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*/
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arm_status arm_convolve_1x1_HWC_q7_fast_nonsquare(const q7_t * Im_in,
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const uint16_t dim_im_in_x,
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const uint16_t dim_im_in_y,
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const uint16_t ch_im_in,
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const q7_t * wt,
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const uint16_t ch_im_out,
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const uint16_t dim_kernel_x,
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const uint16_t dim_kernel_y,
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const uint16_t padding_x,
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const uint16_t padding_y,
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const uint16_t stride_x,
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const uint16_t stride_y,
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const q7_t * bias,
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const uint16_t bias_shift,
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const uint16_t out_shift,
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q7_t * Im_out,
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const uint16_t dim_im_out_x,
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const uint16_t dim_im_out_y,
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q15_t * bufferA,
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q7_t * bufferB);
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/**
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* @brief Q7 version of convolution for RGB image
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* @param[in] Im_in pointer to input tensor
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* @param[in] dim_im_in input tensor dimention
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* @param[in] ch_im_in number of input tensor channels
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* @param[in] wt pointer to kernel weights
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* @param[in] ch_im_out number of filters, i.e., output tensor channels
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* @param[in] dim_kernel filter kernel size
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* @param[in] padding padding sizes
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* @param[in] stride convolution stride
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* @param[in] bias pointer to bias
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* @param[in] bias_shift amount of left-shift for bias
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* @param[in] out_shift amount of right-shift for output
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* @param[in,out] Im_out pointer to output tensor
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* @param[in] dim_im_out output tensor dimension
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* @param[in,out] bufferA pointer to buffer space for input
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* @param[in,out] bufferB pointer to buffer space for output
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* @return The function returns either
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* <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
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*
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* This kernel is written exclusively for convolution with ch_im_in
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* equals 3. This applies on the first layer of CNNs which has input
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* image with RGB format.
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*/
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arm_status arm_convolve_HWC_q7_RGB(const q7_t * Im_in,
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const uint16_t dim_im_in,
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const uint16_t ch_im_in,
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const q7_t * wt,
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const uint16_t ch_im_out,
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const uint16_t dim_kernel,
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const uint16_t padding,
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const uint16_t stride,
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const q7_t * bias,
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const uint16_t bias_shift,
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const uint16_t out_shift,
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q7_t * Im_out,
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const uint16_t dim_im_out,
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q15_t * bufferA,
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q7_t * bufferB);
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|
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/**
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* @brief Fast Q15 convolution function
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* @param[in] Im_in pointer to input tensor
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* @param[in] dim_im_in input tensor dimention
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* @param[in] ch_im_in number of input tensor channels
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* @param[in] wt pointer to kernel weights
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* @param[in] ch_im_out number of filters, i.e., output tensor channels
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* @param[in] dim_kernel filter kernel size
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* @param[in] padding padding sizes
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* @param[in] stride convolution stride
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* @param[in] bias pointer to bias
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* @param[in] bias_shift amount of left-shift for bias
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* @param[in] out_shift amount of right-shift for output
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* @param[in,out] Im_out pointer to output tensor
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* @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.
|
|
*
|
|
* 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_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);
|
|
|
|
/**
|
|
* @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);
|
|
|
|
/**
|
|
* @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.
|
|
*
|
|
* 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(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);
|
|
|
|
/**
|
|
* @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);
|
|
|
|
|
|
/**
|
|
* @defgroup FC Fully-connected Layer Functions
|
|
*
|
|
* Perform fully-connected layer
|
|
*
|
|
* Fully-connected layer is basically a matrix-vector multiplication
|
|
* with bias. The matrix is the weights and the input/output vectors
|
|
* are the activation values. Supported {weight, activation} precisions
|
|
* include {8-bit, 8-bit}, {16-bit, 16-bit}, and {8-bit, 16-bit}.
|
|
*
|
|
* Here we have two types of kernel functions. The basic function
|
|
* implements the function using regular GEMV approach. The opt functions
|
|
* operates with weights in interleaved formats.
|
|
*
|
|
*/
|
|
|
|
/**
|
|
* @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>
|
|
*
|
|
*/
|
|
|
|
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);
|
|
|
|
/**
|
|
* @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>
|
|
*
|
|
*/
|
|
|
|
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);
|
|
|
|
/**
|
|
* @brief Q15 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>
|
|
*
|
|
*/
|
|
|
|
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);
|
|
|
|
/**
|
|
* @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>
|
|
*
|
|
*/
|
|
|
|
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);
|
|
|
|
/**
|
|
* @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>
|
|
*
|
|
*/
|
|
|
|
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);
|
|
|
|
/**
|
|
* @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>
|
|
*
|
|
*/
|
|
|
|
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);
|
|
|
|
/**
|
|
* @brief Matrix-Multiplication Kernels for Convolution
|
|
*
|
|
* These functions are used within convolution layer functions for
|
|
* matrix multiplication.
|
|
*
|
|
* The implementation is similar to CMSIS-DSP arm_mat_mult functions
|
|
* with one Q7 and one Q15 operands. The Q15 operand is the im2col
|
|
* output which is always with 2 columns.
|
|
*
|
|
*/
|
|
|
|
/**
|
|
* @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
|
|
*/
|
|
|
|
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);
|
|
|
|
/**
|
|
* @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
|
|
*/
|
|
|
|
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);
|
|
|
|
#ifdef __cplusplus
|
|
}
|
|
#endif
|
|
|
|
/*
|
|
* Other functions
|
|
* These layers are typically not timing critical
|
|
* Basic implementation is supported here
|
|
*/
|
|
|
|
#ifdef __cplusplus
|
|
extern "C"
|
|
{
|
|
#endif
|
|
|
|
/**
|
|
* @defgroup Acti Neural Network Activation Functions
|
|
*
|
|
* Perform activation layers, including ReLU (Rectified Linear Unit),
|
|
* sigmoid and tanh
|
|
*
|
|
*/
|
|
|
|
/**
|
|
* @brief Q7 RELU function
|
|
* @param[in,out] data pointer to input
|
|
* @param[in] size number of elements
|
|
* @return none.
|
|
*/
|
|
|
|
void arm_relu_q7(q7_t * data, uint16_t size);
|
|
|
|
/**
|
|
* @brief Q15 RELU function
|
|
* @param[in,out] data pointer to input
|
|
* @param[in] size number of elements
|
|
* @return none.
|
|
*/
|
|
|
|
void arm_relu_q15(q15_t * data, uint16_t size);
|
|
|
|
/**
|
|
* @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.
|
|
*/
|
|
|
|
void arm_nn_activations_direct_q7(q7_t * data, uint16_t size, uint16_t int_width,
|
|
arm_nn_activation_type type);
|
|
|
|
/**
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* @brief Q15 neural network activation function using direct table look-up
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* @param[in,out] data pointer to input
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* @param[in] size number of elements
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* @param[in] int_width bit-width of the integer part, assume to be smaller than 3
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* @param[in] type type of activation functions
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* @return none.
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*/
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void arm_nn_activations_direct_q15(q15_t * data, uint16_t size, uint16_t int_width,
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arm_nn_activation_type type);
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/**
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* @defgroup Pooling Neural Network Pooling Functions
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*
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* Perform pooling functions, including max pooling and average pooling
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*
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*/
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/**
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* @brief Q7 max pooling function
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* @param[in] Im_in pointer to input tensor
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* @param[in] dim_im_in input tensor dimention
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* @param[in] ch_im_in number of input tensor channels
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* @param[in] dim_kernel filter kernel size
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* @param[in] padding padding sizes
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* @param[in] stride convolution stride
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* @param[in] dim_im_out output tensor dimension
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* @param[in,out] bufferA pointer to buffer space for input
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* @param[in,out] Im_out pointer to output tensor
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* @return none.
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*
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*/
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void arm_maxpool_q7_HWC(q7_t * Im_in,
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const uint16_t dim_im_in,
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const uint16_t ch_im_in,
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const uint16_t dim_kernel,
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const uint16_t padding,
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const uint16_t stride,
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const uint16_t dim_im_out,
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q7_t * bufferA,
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q7_t * Im_out);
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/**
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* @brief Q7 average pooling function
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* @param[in] Im_in pointer to input tensor
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* @param[in] dim_im_in input tensor dimention
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* @param[in] ch_im_in number of input tensor channels
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* @param[in] dim_kernel filter kernel size
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* @param[in] padding padding sizes
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* @param[in] stride convolution stride
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* @param[in] dim_im_out output tensor dimension
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* @param[in,out] bufferA pointer to buffer space for input
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* @param[in,out] Im_out pointer to output tensor
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* @return none.
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*
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*/
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void arm_avepool_q7_HWC(q7_t * Im_in,
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const uint16_t dim_im_in,
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const uint16_t ch_im_in,
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const uint16_t dim_kernel,
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const uint16_t padding,
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const uint16_t stride,
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const uint16_t dim_im_out,
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q7_t * bufferA,
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q7_t * Im_out);
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/**
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* @defgroup Softmax Softmax Functions
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*
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* EXP(2) based softmax function
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*
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*/
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/**
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* @brief Q7 softmax function
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* @param[in] vec_in pointer to input vector
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* @param[in] dim_vec input vector dimention
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* @param[out] p_out pointer to output vector
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* @return none.
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*
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*/
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void arm_softmax_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out);
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/**
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* @brief Q15 softmax function
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* @param[in] vec_in pointer to input vector
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* @param[in] dim_vec input vector dimention
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* @param[out] p_out pointer to output vector
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* @return none.
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*
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*/
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void arm_softmax_q15(const q15_t * vec_in, const uint16_t dim_vec, q15_t * p_out);
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#ifdef __cplusplus
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}
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#endif
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#endif
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