1 | /*
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2 | * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
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3 | *
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4 | * SPDX-License-Identifier: Apache-2.0
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5 | *
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6 | * Licensed under the Apache License, Version 2.0 (the License); you may
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7 | * not use this file except in compliance with the License.
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8 | * You may obtain a copy of the License at
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9 | *
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10 | * www.apache.org/licenses/LICENSE-2.0
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11 | *
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12 | * Unless required by applicable law or agreed to in writing, software
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13 | * distributed under the License is distributed on an AS IS BASIS, WITHOUT
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14 | * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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15 | * See the License for the specific language governing permissions and
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16 | * limitations under the License.
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17 | */
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18 |
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19 | /* ----------------------------------------------------------------------
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20 | * Project: CMSIS NN Library
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21 | * Title: arm_nnfunctions.h
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22 | * Description: Public header file for CMSIS NN Library
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23 | *
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24 | * $Date: 13. July 2018
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25 | * $Revision: V.1.0.0
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26 | *
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27 | * Target Processor: Cortex-M cores
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28 | * -------------------------------------------------------------------- */
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29 |
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30 | /**
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31 | \mainpage CMSIS NN Software Library
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32 | *
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33 | * Introduction
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34 | * ------------
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35 | *
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36 | * This user manual describes the CMSIS NN software library,
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37 | * a collection of efficient neural network kernels developed to maximize the
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38 | * performance and minimize the memory footprint of neural networks on Cortex-M processor cores.
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39 | *
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40 | * The library is divided into a number of functions each covering a specific category:
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41 | * - Neural Network Convolution Functions
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42 | * - Neural Network Activation Functions
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43 | * - Fully-connected Layer Functions
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44 | * - Neural Network Pooling Functions
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45 | * - Softmax Functions
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46 | * - Neural Network Support Functions
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47 | *
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48 | * The library has separate functions for operating on different weight and activation data
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49 | * types including 8-bit integers (q7_t) and 16-bit integers (q15_t). The descrition of the
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50 | * kernels are included in the function description. The implementation details are also
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51 | * described in this paper [1].
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52 | *
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53 | * Block Diagram
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54 | * --------
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55 | * \image html CMSIS-NN-OVERVIEW.PNG
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56 | *
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57 | * Examples
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58 | * --------
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59 | *
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60 | * The library ships with a number of examples which demonstrate how to use the library functions.
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61 | *
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62 | * Pre-processor Macros
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63 | * ------------
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64 | *
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65 | * Each library project have differant pre-processor macros.
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66 | *
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67 | * - ARM_MATH_DSP:
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68 | *
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69 | * Define macro ARM_MATH_DSP, If the silicon supports DSP instructions.
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70 | *
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71 | * - ARM_MATH_BIG_ENDIAN:
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72 | *
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73 | * 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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74 | *
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75 | * - ARM_NN_TRUNCATE:
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76 | *
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77 | * Define macro ARM_NN_TRUNCATE to use floor instead of round-to-the-nearest-int for the computation.
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78 | *
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79 | * Copyright Notice
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80 | * ------------
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81 | *
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82 | * Copyright (C) 2010-2018 Arm Limited. All rights reserved.
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83 | *
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84 | * [1] CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs https://arxiv.org/abs/1801.06601
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85 | */
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86 |
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87 | /**
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88 | * @defgroup groupNN Neural Network Functions
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89 | * These functions perform basic operations for neural network layers.
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90 | */
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91 |
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92 | #ifndef _ARM_NNFUNCTIONS_H
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93 | #define _ARM_NNFUNCTIONS_H
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94 |
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95 | #include "arm_nnsupportfunctions.h"
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96 | #include "arm_nn_tables.h"
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97 |
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98 | #define USE_INTRINSIC
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99 |
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100 | //#define ARM_NN_TRUNCATE /* This config the rounding model to floor or round to the nearest int */
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101 |
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102 | #ifdef __cplusplus
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103 | extern "C"
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104 | {
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105 | #endif
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106 |
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107 | /**
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108 | * @defgroup NNConv Neural Network Convolution Functions
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109 | *
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110 | * Perform convolution layer
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111 | *
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112 | * The convolution is implemented in 2 steps: im2col and GEMM
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113 | *
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114 | * im2col is a process of converting each patch of image data into
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115 | * a column. After im2col, the convolution is computed as matrix-matrix
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116 | * multiplication.
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117 | *
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118 | * To reduce the memory footprint, the im2col is performed partially.
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119 | * Each iteration, only a few column (i.e., patches) are generated and
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120 | * computed with GEMM kernels similar to CMSIS-DSP arm_mat_mult functions.
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121 | *
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122 | */
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123 |
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124 | /**
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125 | * @brief Basic Q7 convolution function
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126 | * @param[in] Im_in pointer to input tensor
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127 | * @param[in] dim_im_in input tensor dimention
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128 | * @param[in] ch_im_in number of input tensor channels
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129 | * @param[in] wt pointer to kernel weights
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130 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
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131 | * @param[in] dim_kernel filter kernel size
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132 | * @param[in] padding padding sizes
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133 | * @param[in] stride convolution stride
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134 | * @param[in] bias pointer to bias
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135 | * @param[in] bias_shift amount of left-shift for bias
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136 | * @param[in] out_shift amount of right-shift for output
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137 | * @param[in,out] Im_out pointer to output tensor
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138 | * @param[in] dim_im_out output tensor dimension
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139 | * @param[in,out] bufferA pointer to buffer space for input
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140 | * @param[in,out] bufferB pointer to buffer space for output
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141 | * @return The function returns <code>ARM_MATH_SUCCESS</code>
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142 | *
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143 | */
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144 |
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145 | arm_status arm_convolve_HWC_q7_basic(const q7_t * Im_in,
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146 | const uint16_t dim_im_in,
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147 | const uint16_t ch_im_in,
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148 | const q7_t * wt,
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149 | const uint16_t ch_im_out,
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150 | const uint16_t dim_kernel,
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151 | const uint16_t padding,
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152 | const uint16_t stride,
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153 | const q7_t * bias,
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154 | const uint16_t bias_shift,
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155 | const uint16_t out_shift,
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156 | q7_t * Im_out,
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157 | const uint16_t dim_im_out,
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158 | q15_t * bufferA,
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159 | q7_t * bufferB);
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160 |
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161 | /**
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162 | * @brief Basic Q7 convolution function (non-sqaure shape)
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163 | * @param[in] Im_in pointer to input tensor
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164 | * @param[in] dim_im_in_x input tensor dimention x
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165 | * @param[in] dim_im_in_y input tensor dimention y
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166 | * @param[in] ch_im_in number of input tensor channels
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167 | * @param[in] wt pointer to kernel weights
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168 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
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169 | * @param[in] dim_kernel_x filter kernel size x
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170 | * @param[in] dim_kernel_y filter kernel size y
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171 | * @param[in] padding_x padding size x
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172 | * @param[in] padding_y padding size y
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173 | * @param[in] stride_x convolution stride x
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174 | * @param[in] stride_y convolution stride y
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175 | * @param[in] bias pointer to bias
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176 | * @param[in] bias_shift amount of left-shift for bias
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177 | * @param[in] out_shift amount of right-shift for output
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178 | * @param[in,out] Im_out pointer to output tensor
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179 | * @param[in] dim_im_out_x output tensor dimension x
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180 | * @param[in] dim_im_out_y output tensor dimension y
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181 | * @param[in,out] bufferA pointer to buffer space for input
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182 | * @param[in,out] bufferB pointer to buffer space for output
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183 | * @return The function returns <code>ARM_MATH_SUCCESS</code>
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184 | */
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185 |
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186 | arm_status arm_convolve_HWC_q7_basic_nonsquare(const q7_t * Im_in,
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187 | const uint16_t dim_im_in_x,
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188 | const uint16_t dim_im_in_y,
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189 | const uint16_t ch_im_in,
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190 | const q7_t * wt,
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191 | const uint16_t ch_im_out,
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192 | const uint16_t dim_kernel_x,
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193 | const uint16_t dim_kernel_y,
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194 | const uint16_t padding_x,
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195 | const uint16_t padding_y,
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196 | const uint16_t stride_x,
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197 | const uint16_t stride_y,
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198 | const q7_t * bias,
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199 | const uint16_t bias_shift,
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200 | const uint16_t out_shift,
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201 | q7_t * Im_out,
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202 | const uint16_t dim_im_out_x,
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203 | const uint16_t dim_im_out_y,
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204 | q15_t * bufferA,
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205 | q7_t * bufferB);
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206 |
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207 | /**
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208 | * @brief Basic Q15 convolution function
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209 | * @param[in] Im_in pointer to input tensor
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210 | * @param[in] dim_im_in input tensor dimention
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211 | * @param[in] ch_im_in number of input tensor channels
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212 | * @param[in] wt pointer to kernel weights
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213 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
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214 | * @param[in] dim_kernel filter kernel size
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215 | * @param[in] padding padding sizes
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216 | * @param[in] stride convolution stride
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217 | * @param[in] bias pointer to bias
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218 | * @param[in] bias_shift amount of left-shift for bias
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219 | * @param[in] out_shift amount of right-shift for output
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220 | * @param[in,out] Im_out pointer to output tensor
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221 | * @param[in] dim_im_out output tensor dimension
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222 | * @param[in,out] bufferA pointer to buffer space for input
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223 | * @param[in,out] bufferB pointer to buffer space for output
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224 | * @return The function returns <code>ARM_MATH_SUCCESS</code>
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225 | *
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226 | */
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227 |
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228 | arm_status arm_convolve_HWC_q15_basic(const q15_t * Im_in,
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229 | const uint16_t dim_im_in,
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230 | const uint16_t ch_im_in,
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231 | const q15_t * wt,
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232 | const uint16_t ch_im_out,
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233 | const uint16_t dim_kernel,
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234 | const uint16_t padding,
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235 | const uint16_t stride,
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236 | const q15_t * bias,
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237 | const uint16_t bias_shift,
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238 | const uint16_t out_shift,
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239 | q15_t * Im_out,
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240 | const uint16_t dim_im_out,
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241 | q15_t * bufferA,
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242 | q7_t * bufferB);
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243 |
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244 | /**
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245 | * @brief Fast Q7 convolution function
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246 | * @param[in] Im_in pointer to input tensor
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247 | * @param[in] dim_im_in input tensor dimention
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248 | * @param[in] ch_im_in number of input tensor channels
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249 | * @param[in] wt pointer to kernel weights
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250 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
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251 | * @param[in] dim_kernel filter kernel size
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252 | * @param[in] padding padding sizes
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253 | * @param[in] stride convolution stride
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254 | * @param[in] bias pointer to bias
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255 | * @param[in] bias_shift amount of left-shift for bias
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256 | * @param[in] out_shift amount of right-shift for output
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257 | * @param[in,out] Im_out pointer to output tensor
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258 | * @param[in] dim_im_out output tensor dimension
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259 | * @param[in,out] bufferA pointer to buffer space for input
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260 | * @param[in,out] bufferB pointer to buffer space for output
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261 | * @return The function returns either
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262 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
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263 | *
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264 | * This function is the version with full list of optimization tricks, but with
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265 | * some contraints:
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266 | * ch_im_in is multiple of 4
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267 | * ch_im_out is multiple of 2
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268 | */
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269 |
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270 | arm_status arm_convolve_HWC_q7_fast(const q7_t * Im_in,
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271 | const uint16_t dim_im_in,
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272 | const uint16_t ch_im_in,
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273 | const q7_t * wt,
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274 | const uint16_t ch_im_out,
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275 | const uint16_t dim_kernel,
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276 | const uint16_t padding,
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277 | const uint16_t stride,
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278 | const q7_t * bias,
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279 | const uint16_t bias_shift,
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280 | const uint16_t out_shift,
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281 | q7_t * Im_out,
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282 | const uint16_t dim_im_out,
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283 | q15_t * bufferA,
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284 | q7_t * bufferB);
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285 |
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286 | /**
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287 | * @brief Fast Q7 convolution function (non-sqaure shape)
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288 | * @param[in] Im_in pointer to input tensor
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289 | * @param[in] dim_im_in_x input tensor dimention x
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290 | * @param[in] dim_im_in_y input tensor dimention y
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291 | * @param[in] ch_im_in number of input tensor channels
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292 | * @param[in] wt pointer to kernel weights
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293 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
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294 | * @param[in] dim_kernel_x filter kernel size x
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295 | * @param[in] dim_kernel_y filter kernel size y
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296 | * @param[in] padding_x padding size x
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297 | * @param[in] padding_y padding size y
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298 | * @param[in] stride_x convolution stride x
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299 | * @param[in] stride_y convolution stride y
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300 | * @param[in] bias pointer to bias
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301 | * @param[in] bias_shift amount of left-shift for bias
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302 | * @param[in] out_shift amount of right-shift for output
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303 | * @param[in,out] Im_out pointer to output tensor
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304 | * @param[in] dim_im_out_x output tensor dimension x
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305 | * @param[in] dim_im_out_y output tensor dimension y
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306 | * @param[in,out] bufferA pointer to buffer space for input
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307 | * @param[in,out] bufferB pointer to buffer space for output
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308 | * @return The function returns either
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309 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
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310 | *
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311 | * This function is the version with full list of optimization tricks, but with
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312 | * some contraints:
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313 | * ch_im_in is multiple of 4
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314 | * ch_im_out is multiple of 2
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315 | */
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316 |
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317 | arm_status arm_convolve_HWC_q7_fast_nonsquare(const q7_t * Im_in,
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318 | const uint16_t dim_im_in_x,
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319 | const uint16_t dim_im_in_y,
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320 | const uint16_t ch_im_in,
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321 | const q7_t * wt,
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322 | const uint16_t ch_im_out,
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323 | const uint16_t dim_kernel_x,
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324 | const uint16_t dim_kernel_y,
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325 | const uint16_t padding_x,
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326 | const uint16_t padding_y,
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327 | const uint16_t stride_x,
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328 | const uint16_t stride_y,
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329 | const q7_t * bias,
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330 | const uint16_t bias_shift,
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331 | const uint16_t out_shift,
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332 | q7_t * Im_out,
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333 | const uint16_t dim_im_out_x,
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334 | const uint16_t dim_im_out_y,
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335 | q15_t * bufferA,
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336 | q7_t * bufferB);
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337 |
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338 | /**
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339 | * @brief Fast Q7 version of 1x1 convolution (non-sqaure shape)
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340 | * @param[in] Im_in pointer to input tensor
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341 | * @param[in] dim_im_in_x input tensor dimention x
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342 | * @param[in] dim_im_in_y input tensor dimention y
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343 | * @param[in] ch_im_in number of input tensor channels
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344 | * @param[in] wt pointer to kernel weights
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345 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
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346 | * @param[in] dim_kernel_x filter kernel size x
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347 | * @param[in] dim_kernel_y filter kernel size y
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348 | * @param[in] padding_x padding size x
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349 | * @param[in] padding_y padding size y
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350 | * @param[in] stride_x convolution stride x
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351 | * @param[in] stride_y convolution stride y
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352 | * @param[in] bias pointer to bias
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353 | * @param[in] bias_shift amount of left-shift for bias
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354 | * @param[in] out_shift amount of right-shift for output
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355 | * @param[in,out] Im_out pointer to output tensor
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356 | * @param[in] dim_im_out_x output tensor dimension x
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357 | * @param[in] dim_im_out_y output tensor dimension y
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358 | * @param[in,out] bufferA pointer to buffer space for input
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359 | * @param[in,out] bufferB pointer to buffer space for output
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360 | * @return The function returns either
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361 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
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362 | *
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363 | * This function implement convolution with 1x1 kernel size (i.e., dim_kernel_x=1
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364 | * and dim_kernel_y=1). It can be used for
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365 | * second half of MobileNets after depthwise separable convolution.
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366 | *
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367 | * This function is the version with full list of optimization tricks, but with
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368 | * some contraints:
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369 | * ch_im_in is multiple of 4
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370 | * ch_im_out is multiple of 2
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371 | */
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372 | arm_status arm_convolve_1x1_HWC_q7_fast_nonsquare(const q7_t * Im_in,
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373 | const uint16_t dim_im_in_x,
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374 | const uint16_t dim_im_in_y,
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375 | const uint16_t ch_im_in,
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376 | const q7_t * wt,
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377 | const uint16_t ch_im_out,
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378 | const uint16_t dim_kernel_x,
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379 | const uint16_t dim_kernel_y,
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380 | const uint16_t padding_x,
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381 | const uint16_t padding_y,
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382 | const uint16_t stride_x,
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383 | const uint16_t stride_y,
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384 | const q7_t * bias,
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385 | const uint16_t bias_shift,
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386 | const uint16_t out_shift,
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387 | q7_t * Im_out,
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388 | const uint16_t dim_im_out_x,
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389 | const uint16_t dim_im_out_y,
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390 | q15_t * bufferA,
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391 | q7_t * bufferB);
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392 |
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393 | /**
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394 | * @brief Q7 version of convolution for RGB image
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395 | * @param[in] Im_in pointer to input tensor
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396 | * @param[in] dim_im_in input tensor dimention
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397 | * @param[in] ch_im_in number of input tensor channels
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398 | * @param[in] wt pointer to kernel weights
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399 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
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400 | * @param[in] dim_kernel filter kernel size
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401 | * @param[in] padding padding sizes
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402 | * @param[in] stride convolution stride
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403 | * @param[in] bias pointer to bias
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404 | * @param[in] bias_shift amount of left-shift for bias
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405 | * @param[in] out_shift amount of right-shift for output
|
---|
406 | * @param[in,out] Im_out pointer to output tensor
|
---|
407 | * @param[in] dim_im_out output tensor dimension
|
---|
408 | * @param[in,out] bufferA pointer to buffer space for input
|
---|
409 | * @param[in,out] bufferB pointer to buffer space for output
|
---|
410 | * @return The function returns either
|
---|
411 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
|
---|
412 | *
|
---|
413 | * This kernel is written exclusively for convolution with ch_im_in
|
---|
414 | * equals 3. This applies on the first layer of CNNs which has input
|
---|
415 | * image with RGB format.
|
---|
416 | */
|
---|
417 |
|
---|
418 | arm_status arm_convolve_HWC_q7_RGB(const q7_t * Im_in,
|
---|
419 | const uint16_t dim_im_in,
|
---|
420 | const uint16_t ch_im_in,
|
---|
421 | const q7_t * wt,
|
---|
422 | const uint16_t ch_im_out,
|
---|
423 | const uint16_t dim_kernel,
|
---|
424 | const uint16_t padding,
|
---|
425 | const uint16_t stride,
|
---|
426 | const q7_t * bias,
|
---|
427 | const uint16_t bias_shift,
|
---|
428 | const uint16_t out_shift,
|
---|
429 | q7_t * Im_out,
|
---|
430 | const uint16_t dim_im_out,
|
---|
431 | q15_t * bufferA,
|
---|
432 | q7_t * bufferB);
|
---|
433 |
|
---|
434 | /**
|
---|
435 | * @brief Fast Q15 convolution function
|
---|
436 | * @param[in] Im_in pointer to input tensor
|
---|
437 | * @param[in] dim_im_in input tensor dimention
|
---|
438 | * @param[in] ch_im_in number of input tensor channels
|
---|
439 | * @param[in] wt pointer to kernel weights
|
---|
440 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
|
---|
441 | * @param[in] dim_kernel filter kernel size
|
---|
442 | * @param[in] padding padding sizes
|
---|
443 | * @param[in] stride convolution stride
|
---|
444 | * @param[in] bias pointer to bias
|
---|
445 | * @param[in] bias_shift amount of left-shift for bias
|
---|
446 | * @param[in] out_shift amount of right-shift for output
|
---|
447 | * @param[in,out] Im_out pointer to output tensor
|
---|
448 | * @param[in] dim_im_out output tensor dimension
|
---|
449 | * @param[in,out] bufferA pointer to buffer space for input
|
---|
450 | * @param[in,out] bufferB pointer to buffer space for output
|
---|
451 | * @return The function returns either
|
---|
452 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
|
---|
453 | *
|
---|
454 | * This function is the version with full list of optimization tricks, but with
|
---|
455 | * some contraints:
|
---|
456 | * ch_im_in is multiple of 2
|
---|
457 | * ch_im_out is multiple of 2
|
---|
458 | */
|
---|
459 |
|
---|
460 | arm_status arm_convolve_HWC_q15_fast(const q15_t * Im_in,
|
---|
461 | const uint16_t dim_im_in,
|
---|
462 | const uint16_t ch_im_in,
|
---|
463 | const q15_t * wt,
|
---|
464 | const uint16_t ch_im_out,
|
---|
465 | const uint16_t dim_kernel,
|
---|
466 | const uint16_t padding,
|
---|
467 | const uint16_t stride,
|
---|
468 | const q15_t * bias,
|
---|
469 | const uint16_t bias_shift,
|
---|
470 | const uint16_t out_shift,
|
---|
471 | q15_t * Im_out,
|
---|
472 | const uint16_t dim_im_out,
|
---|
473 | q15_t * bufferA,
|
---|
474 | q7_t * bufferB);
|
---|
475 |
|
---|
476 | /**
|
---|
477 | * @brief Fast Q15 convolution function (non-sqaure shape)
|
---|
478 | * @param[in] Im_in pointer to input tensor
|
---|
479 | * @param[in] dim_im_in_x input tensor dimention x
|
---|
480 | * @param[in] dim_im_in_y input tensor dimention y
|
---|
481 | * @param[in] ch_im_in number of input tensor channels
|
---|
482 | * @param[in] wt pointer to kernel weights
|
---|
483 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
|
---|
484 | * @param[in] dim_kernel_x filter kernel size x
|
---|
485 | * @param[in] dim_kernel_y filter kernel size y
|
---|
486 | * @param[in] padding_x padding size x
|
---|
487 | * @param[in] padding_y padding size y
|
---|
488 | * @param[in] stride_x convolution stride x
|
---|
489 | * @param[in] stride_y convolution stride y
|
---|
490 | * @param[in] bias pointer to bias
|
---|
491 | * @param[in] bias_shift amount of left-shift for bias
|
---|
492 | * @param[in] out_shift amount of right-shift for output
|
---|
493 | * @param[in,out] Im_out pointer to output tensor
|
---|
494 | * @param[in] dim_im_out_x output tensor dimension x
|
---|
495 | * @param[in] dim_im_out_y output tensor dimension y
|
---|
496 | * @param[in,out] bufferA pointer to buffer space for input
|
---|
497 | * @param[in,out] bufferB pointer to buffer space for output
|
---|
498 | * @return The function returns either
|
---|
499 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
|
---|
500 | *
|
---|
501 | * @details
|
---|
502 | *
|
---|
503 | * <b>Buffer size:</b>
|
---|
504 | *
|
---|
505 | * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
|
---|
506 | *
|
---|
507 | * bufferB size: 0
|
---|
508 | *
|
---|
509 | * <b>Input dimension constraints:</b>
|
---|
510 | *
|
---|
511 | * ch_im_in is multiple of 2
|
---|
512 | *
|
---|
513 | * ch_im_out is multipe of 2
|
---|
514 | *
|
---|
515 | */
|
---|
516 |
|
---|
517 | arm_status
|
---|
518 | arm_convolve_HWC_q15_fast_nonsquare(const q15_t * Im_in,
|
---|
519 | const uint16_t dim_im_in_x,
|
---|
520 | const uint16_t dim_im_in_y,
|
---|
521 | const uint16_t ch_im_in,
|
---|
522 | const q15_t * wt,
|
---|
523 | const uint16_t ch_im_out,
|
---|
524 | const uint16_t dim_kernel_x,
|
---|
525 | const uint16_t dim_kernel_y,
|
---|
526 | const uint16_t padding_x,
|
---|
527 | const uint16_t padding_y,
|
---|
528 | const uint16_t stride_x,
|
---|
529 | const uint16_t stride_y,
|
---|
530 | const q15_t * bias,
|
---|
531 | const uint16_t bias_shift,
|
---|
532 | const uint16_t out_shift,
|
---|
533 | q15_t * Im_out,
|
---|
534 | const uint16_t dim_im_out_x,
|
---|
535 | const uint16_t dim_im_out_y,
|
---|
536 | q15_t * bufferA,
|
---|
537 | q7_t * bufferB);
|
---|
538 |
|
---|
539 | /**
|
---|
540 | * @brief Q7 depthwise separable convolution function
|
---|
541 | * @param[in] Im_in pointer to input tensor
|
---|
542 | * @param[in] dim_im_in input tensor dimention
|
---|
543 | * @param[in] ch_im_in number of input tensor channels
|
---|
544 | * @param[in] wt pointer to kernel weights
|
---|
545 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
|
---|
546 | * @param[in] dim_kernel filter kernel size
|
---|
547 | * @param[in] padding padding sizes
|
---|
548 | * @param[in] stride convolution stride
|
---|
549 | * @param[in] bias pointer to bias
|
---|
550 | * @param[in] bias_shift amount of left-shift for bias
|
---|
551 | * @param[in] out_shift amount of right-shift for output
|
---|
552 | * @param[in,out] Im_out pointer to output tensor
|
---|
553 | * @param[in] dim_im_out output tensor dimension
|
---|
554 | * @param[in,out] bufferA pointer to buffer space for input
|
---|
555 | * @param[in,out] bufferB pointer to buffer space for output
|
---|
556 | * @return The function returns either
|
---|
557 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
|
---|
558 | *
|
---|
559 | * This function is the version with full list of optimization tricks, but with
|
---|
560 | * some contraints:
|
---|
561 | * ch_im_in is multiple of 2
|
---|
562 | * ch_im_out is multiple of 2
|
---|
563 | */
|
---|
564 |
|
---|
565 | arm_status arm_depthwise_separable_conv_HWC_q7(const q7_t * Im_in,
|
---|
566 | const uint16_t dim_im_in,
|
---|
567 | const uint16_t ch_im_in,
|
---|
568 | const q7_t * wt,
|
---|
569 | const uint16_t ch_im_out,
|
---|
570 | const uint16_t dim_kernel,
|
---|
571 | const uint16_t padding,
|
---|
572 | const uint16_t stride,
|
---|
573 | const q7_t * bias,
|
---|
574 | const uint16_t bias_shift,
|
---|
575 | const uint16_t out_shift,
|
---|
576 | q7_t * Im_out,
|
---|
577 | const uint16_t dim_im_out,
|
---|
578 | q15_t * bufferA,
|
---|
579 | q7_t * bufferB);
|
---|
580 |
|
---|
581 | /**
|
---|
582 | * @brief Q7 depthwise separable convolution function (non-square shape)
|
---|
583 | * @param[in] Im_in pointer to input tensor
|
---|
584 | * @param[in] dim_im_in_x input tensor dimention x
|
---|
585 | * @param[in] dim_im_in_y input tensor dimention y
|
---|
586 | * @param[in] ch_im_in number of input tensor channels
|
---|
587 | * @param[in] wt pointer to kernel weights
|
---|
588 | * @param[in] ch_im_out number of filters, i.e., output tensor channels
|
---|
589 | * @param[in] dim_kernel_x filter kernel size x
|
---|
590 | * @param[in] dim_kernel_y filter kernel size y
|
---|
591 | * @param[in] padding_x padding sizes x
|
---|
592 | * @param[in] padding_y padding sizes y
|
---|
593 | * @param[in] stride_x convolution stride x
|
---|
594 | * @param[in] stride_y convolution stride y
|
---|
595 | * @param[in] bias pointer to bias
|
---|
596 | * @param[in] bias_shift amount of left-shift for bias
|
---|
597 | * @param[in] out_shift amount of right-shift for output
|
---|
598 | * @param[in,out] Im_out pointer to output tensor
|
---|
599 | * @param[in] dim_im_out_x output tensor dimension x
|
---|
600 | * @param[in] dim_im_out_y output tensor dimension y
|
---|
601 | * @param[in,out] bufferA pointer to buffer space for input
|
---|
602 | * @param[in,out] bufferB pointer to buffer space for output
|
---|
603 | * @return The function returns either
|
---|
604 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
|
---|
605 | *
|
---|
606 | * This function is the version with full list of optimization tricks, but with
|
---|
607 | * some contraints:
|
---|
608 | * ch_im_in is multiple of 2
|
---|
609 | * ch_im_out is multiple of 2
|
---|
610 | */
|
---|
611 | arm_status arm_depthwise_separable_conv_HWC_q7_nonsquare(const q7_t * Im_in,
|
---|
612 | const uint16_t dim_im_in_x,
|
---|
613 | const uint16_t dim_im_in_y,
|
---|
614 | const uint16_t ch_im_in,
|
---|
615 | const q7_t * wt,
|
---|
616 | const uint16_t ch_im_out,
|
---|
617 | const uint16_t dim_kernel_x,
|
---|
618 | const uint16_t dim_kernel_y,
|
---|
619 | const uint16_t padding_x,
|
---|
620 | const uint16_t padding_y,
|
---|
621 | const uint16_t stride_x,
|
---|
622 | const uint16_t stride_y,
|
---|
623 | const q7_t * bias,
|
---|
624 | const uint16_t bias_shift,
|
---|
625 | const uint16_t out_shift,
|
---|
626 | q7_t * Im_out,
|
---|
627 | const uint16_t dim_im_out_x,
|
---|
628 | const uint16_t dim_im_out_y,
|
---|
629 | q15_t * bufferA,
|
---|
630 | q7_t * bufferB);
|
---|
631 |
|
---|
632 |
|
---|
633 | /**
|
---|
634 | * @defgroup FC Fully-connected Layer Functions
|
---|
635 | *
|
---|
636 | * Perform fully-connected layer
|
---|
637 | *
|
---|
638 | * Fully-connected layer is basically a matrix-vector multiplication
|
---|
639 | * with bias. The matrix is the weights and the input/output vectors
|
---|
640 | * are the activation values. Supported {weight, activation} precisions
|
---|
641 | * include {8-bit, 8-bit}, {16-bit, 16-bit}, and {8-bit, 16-bit}.
|
---|
642 | *
|
---|
643 | * Here we have two types of kernel functions. The basic function
|
---|
644 | * implements the function using regular GEMV approach. The opt functions
|
---|
645 | * operates with weights in interleaved formats.
|
---|
646 | *
|
---|
647 | */
|
---|
648 |
|
---|
649 | /**
|
---|
650 | * @brief Q7 basic fully-connected layer function
|
---|
651 | * @param[in] pV pointer to input vector
|
---|
652 | * @param[in] pM pointer to matrix weights
|
---|
653 | * @param[in] dim_vec length of the vector
|
---|
654 | * @param[in] num_of_rows number of rows in weight matrix
|
---|
655 | * @param[in] bias_shift amount of left-shift for bias
|
---|
656 | * @param[in] out_shift amount of right-shift for output
|
---|
657 | * @param[in] bias pointer to bias
|
---|
658 | * @param[in,out] pOut pointer to output vector
|
---|
659 | * @param[in,out] vec_buffer pointer to buffer space for input
|
---|
660 | * @return The function returns <code>ARM_MATH_SUCCESS</code>
|
---|
661 | *
|
---|
662 | */
|
---|
663 |
|
---|
664 | arm_status arm_fully_connected_q7(const q7_t * pV,
|
---|
665 | const q7_t * pM,
|
---|
666 | const uint16_t dim_vec,
|
---|
667 | const uint16_t num_of_rows,
|
---|
668 | const uint16_t bias_shift,
|
---|
669 | const uint16_t out_shift,
|
---|
670 | const q7_t * bias,
|
---|
671 | q7_t * pOut,
|
---|
672 | q15_t * vec_buffer);
|
---|
673 |
|
---|
674 | /**
|
---|
675 | * @brief Q7 opt fully-connected layer function
|
---|
676 | * @param[in] pV pointer to input vector
|
---|
677 | * @param[in] pM pointer to matrix weights
|
---|
678 | * @param[in] dim_vec length of the vector
|
---|
679 | * @param[in] num_of_rows number of rows in weight matrix
|
---|
680 | * @param[in] bias_shift amount of left-shift for bias
|
---|
681 | * @param[in] out_shift amount of right-shift for output
|
---|
682 | * @param[in] bias pointer to bias
|
---|
683 | * @param[in,out] pOut pointer to output vector
|
---|
684 | * @param[in,out] vec_buffer pointer to buffer space for input
|
---|
685 | * @return The function returns <code>ARM_MATH_SUCCESS</code>
|
---|
686 | *
|
---|
687 | */
|
---|
688 |
|
---|
689 | arm_status arm_fully_connected_q7_opt(const q7_t * pV,
|
---|
690 | const q7_t * pM,
|
---|
691 | const uint16_t dim_vec,
|
---|
692 | const uint16_t num_of_rows,
|
---|
693 | const uint16_t bias_shift,
|
---|
694 | const uint16_t out_shift,
|
---|
695 | const q7_t * bias,
|
---|
696 | q7_t * pOut,
|
---|
697 | q15_t * vec_buffer);
|
---|
698 |
|
---|
699 | /**
|
---|
700 | * @brief Q15 basic fully-connected layer function
|
---|
701 | * @param[in] pV pointer to input vector
|
---|
702 | * @param[in] pM pointer to matrix weights
|
---|
703 | * @param[in] dim_vec length of the vector
|
---|
704 | * @param[in] num_of_rows number of rows in weight matrix
|
---|
705 | * @param[in] bias_shift amount of left-shift for bias
|
---|
706 | * @param[in] out_shift amount of right-shift for output
|
---|
707 | * @param[in] bias pointer to bias
|
---|
708 | * @param[in,out] pOut pointer to output vector
|
---|
709 | * @param[in,out] vec_buffer pointer to buffer space for input
|
---|
710 | * @return The function returns <code>ARM_MATH_SUCCESS</code>
|
---|
711 | *
|
---|
712 | */
|
---|
713 |
|
---|
714 | arm_status arm_fully_connected_q15(const q15_t * pV,
|
---|
715 | const q15_t * pM,
|
---|
716 | const uint16_t dim_vec,
|
---|
717 | const uint16_t num_of_rows,
|
---|
718 | const uint16_t bias_shift,
|
---|
719 | const uint16_t out_shift,
|
---|
720 | const q15_t * bias,
|
---|
721 | q15_t * pOut,
|
---|
722 | q15_t * vec_buffer);
|
---|
723 |
|
---|
724 | /**
|
---|
725 | * @brief Q15 opt fully-connected layer function
|
---|
726 | * @param[in] pV pointer to input vector
|
---|
727 | * @param[in] pM pointer to matrix weights
|
---|
728 | * @param[in] dim_vec length of the vector
|
---|
729 | * @param[in] num_of_rows number of rows in weight matrix
|
---|
730 | * @param[in] bias_shift amount of left-shift for bias
|
---|
731 | * @param[in] out_shift amount of right-shift for output
|
---|
732 | * @param[in] bias pointer to bias
|
---|
733 | * @param[in,out] pOut pointer to output vector
|
---|
734 | * @param[in,out] vec_buffer pointer to buffer space for input
|
---|
735 | * @return The function returns <code>ARM_MATH_SUCCESS</code>
|
---|
736 | *
|
---|
737 | */
|
---|
738 |
|
---|
739 | arm_status arm_fully_connected_q15_opt(const q15_t * pV,
|
---|
740 | const q15_t * pM,
|
---|
741 | const uint16_t dim_vec,
|
---|
742 | const uint16_t num_of_rows,
|
---|
743 | const uint16_t bias_shift,
|
---|
744 | const uint16_t out_shift,
|
---|
745 | const q15_t * bias,
|
---|
746 | q15_t * pOut,
|
---|
747 | q15_t * vec_buffer);
|
---|
748 |
|
---|
749 | /**
|
---|
750 | * @brief Mixed Q15-Q7 fully-connected layer function
|
---|
751 | * @param[in] pV pointer to input vector
|
---|
752 | * @param[in] pM pointer to matrix weights
|
---|
753 | * @param[in] dim_vec length of the vector
|
---|
754 | * @param[in] num_of_rows number of rows in weight matrix
|
---|
755 | * @param[in] bias_shift amount of left-shift for bias
|
---|
756 | * @param[in] out_shift amount of right-shift for output
|
---|
757 | * @param[in] bias pointer to bias
|
---|
758 | * @param[in,out] pOut pointer to output vector
|
---|
759 | * @param[in,out] vec_buffer pointer to buffer space for input
|
---|
760 | * @return The function returns <code>ARM_MATH_SUCCESS</code>
|
---|
761 | *
|
---|
762 | */
|
---|
763 |
|
---|
764 | arm_status arm_fully_connected_mat_q7_vec_q15(const q15_t * pV,
|
---|
765 | const q7_t * pM,
|
---|
766 | const uint16_t dim_vec,
|
---|
767 | const uint16_t num_of_rows,
|
---|
768 | const uint16_t bias_shift,
|
---|
769 | const uint16_t out_shift,
|
---|
770 | const q7_t * bias,
|
---|
771 | q15_t * pOut,
|
---|
772 | q15_t * vec_buffer);
|
---|
773 |
|
---|
774 | /**
|
---|
775 | * @brief Mixed Q15-Q7 opt fully-connected layer function
|
---|
776 | * @param[in] pV pointer to input vector
|
---|
777 | * @param[in] pM pointer to matrix weights
|
---|
778 | * @param[in] dim_vec length of the vector
|
---|
779 | * @param[in] num_of_rows number of rows in weight matrix
|
---|
780 | * @param[in] bias_shift amount of left-shift for bias
|
---|
781 | * @param[in] out_shift amount of right-shift for output
|
---|
782 | * @param[in] bias pointer to bias
|
---|
783 | * @param[in,out] pOut pointer to output vector
|
---|
784 | * @param[in,out] vec_buffer pointer to buffer space for input
|
---|
785 | * @return The function returns <code>ARM_MATH_SUCCESS</code>
|
---|
786 | *
|
---|
787 | */
|
---|
788 |
|
---|
789 | arm_status arm_fully_connected_mat_q7_vec_q15_opt(const q15_t * pV,
|
---|
790 | const q7_t * pM,
|
---|
791 | const uint16_t dim_vec,
|
---|
792 | const uint16_t num_of_rows,
|
---|
793 | const uint16_t bias_shift,
|
---|
794 | const uint16_t out_shift,
|
---|
795 | const q7_t * bias,
|
---|
796 | q15_t * pOut,
|
---|
797 | q15_t * vec_buffer);
|
---|
798 |
|
---|
799 | /**
|
---|
800 | * @brief Matrix-Multiplication Kernels for Convolution
|
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801 | *
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802 | * These functions are used within convolution layer functions for
|
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803 | * matrix multiplication.
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804 | *
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805 | * The implementation is similar to CMSIS-DSP arm_mat_mult functions
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806 | * with one Q7 and one Q15 operands. The Q15 operand is the im2col
|
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807 | * output which is always with 2 columns.
|
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808 | *
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809 | */
|
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810 |
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811 | /**
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812 | * @brief Matrix-multiplication function for convolution
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813 | * @param[in] pA pointer to operand A
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814 | * @param[in] pInBuffer pointer to operand B, always conssists of 2 vectors
|
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815 | * @param[in] ch_im_out numRow of A
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---|
816 | * @param[in] numCol_A numCol of A
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817 | * @param[in] bias_shift amount of left-shift for bias
|
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818 | * @param[in] out_shift amount of right-shift for output
|
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819 | * @param[in] bias the bias
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820 | * @param[in,out] pOut pointer to output
|
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821 | * @return The function returns the incremented output pointer
|
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822 | */
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823 |
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824 | q7_t *arm_nn_mat_mult_kernel_q7_q15(const q7_t * pA,
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825 | const q15_t * pInBuffer,
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826 | const uint16_t ch_im_out,
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827 | const uint16_t numCol_A,
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---|
828 | const uint16_t bias_shift,
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---|
829 | const uint16_t out_shift,
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830 | const q7_t * bias,
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831 | q7_t * pOut);
|
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832 |
|
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833 | /**
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834 | * @brief Matrix-multiplication function for convolution with reordered columns
|
---|
835 | * @param[in] pA pointer to operand A
|
---|
836 | * @param[in] pInBuffer pointer to operand B, always conssists of 2 vectors
|
---|
837 | * @param[in] ch_im_out numRow of A
|
---|
838 | * @param[in] numCol_A numCol of A
|
---|
839 | * @param[in] bias_shift amount of left-shift for bias
|
---|
840 | * @param[in] out_shift amount of right-shift for output
|
---|
841 | * @param[in] bias the bias
|
---|
842 | * @param[in,out] pOut pointer to output
|
---|
843 | * @return The function returns the incremented output pointer
|
---|
844 | */
|
---|
845 |
|
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846 | q7_t *arm_nn_mat_mult_kernel_q7_q15_reordered(const q7_t * pA,
|
---|
847 | const q15_t * pInBuffer,
|
---|
848 | const uint16_t ch_im_out,
|
---|
849 | const uint16_t numCol_A,
|
---|
850 | const uint16_t bias_shift,
|
---|
851 | const uint16_t out_shift,
|
---|
852 | const q7_t * bias,
|
---|
853 | q7_t * pOut);
|
---|
854 |
|
---|
855 | #ifdef __cplusplus
|
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856 | }
|
---|
857 | #endif
|
---|
858 |
|
---|
859 | /*
|
---|
860 | * Other functions
|
---|
861 | * These layers are typically not timing critical
|
---|
862 | * Basic implementation is supported here
|
---|
863 | */
|
---|
864 |
|
---|
865 | #ifdef __cplusplus
|
---|
866 | extern "C"
|
---|
867 | {
|
---|
868 | #endif
|
---|
869 |
|
---|
870 | /**
|
---|
871 | * @defgroup Acti Neural Network Activation Functions
|
---|
872 | *
|
---|
873 | * Perform activation layers, including ReLU (Rectified Linear Unit),
|
---|
874 | * sigmoid and tanh
|
---|
875 | *
|
---|
876 | */
|
---|
877 |
|
---|
878 | /**
|
---|
879 | * @brief Q7 RELU function
|
---|
880 | * @param[in,out] data pointer to input
|
---|
881 | * @param[in] size number of elements
|
---|
882 | * @return none.
|
---|
883 | */
|
---|
884 |
|
---|
885 | void arm_relu_q7(q7_t * data, uint16_t size);
|
---|
886 |
|
---|
887 | /**
|
---|
888 | * @brief Q15 RELU function
|
---|
889 | * @param[in,out] data pointer to input
|
---|
890 | * @param[in] size number of elements
|
---|
891 | * @return none.
|
---|
892 | */
|
---|
893 |
|
---|
894 | void arm_relu_q15(q15_t * data, uint16_t size);
|
---|
895 |
|
---|
896 | /**
|
---|
897 | * @brief Q7 neural network activation function using direct table look-up
|
---|
898 | * @param[in,out] data pointer to input
|
---|
899 | * @param[in] size number of elements
|
---|
900 | * @param[in] int_width bit-width of the integer part, assume to be smaller than 3
|
---|
901 | * @param[in] type type of activation functions
|
---|
902 | * @return none.
|
---|
903 | */
|
---|
904 |
|
---|
905 | void arm_nn_activations_direct_q7(q7_t * data, uint16_t size, uint16_t int_width,
|
---|
906 | arm_nn_activation_type type);
|
---|
907 |
|
---|
908 | /**
|
---|
909 | * @brief Q15 neural network activation function using direct table look-up
|
---|
910 | * @param[in,out] data pointer to input
|
---|
911 | * @param[in] size number of elements
|
---|
912 | * @param[in] int_width bit-width of the integer part, assume to be smaller than 3
|
---|
913 | * @param[in] type type of activation functions
|
---|
914 | * @return none.
|
---|
915 | */
|
---|
916 |
|
---|
917 | void arm_nn_activations_direct_q15(q15_t * data, uint16_t size, uint16_t int_width,
|
---|
918 | arm_nn_activation_type type);
|
---|
919 |
|
---|
920 | /**
|
---|
921 | * @defgroup Pooling Neural Network Pooling Functions
|
---|
922 | *
|
---|
923 | * Perform pooling functions, including max pooling and average pooling
|
---|
924 | *
|
---|
925 | */
|
---|
926 |
|
---|
927 | /**
|
---|
928 | * @brief Q7 max pooling function
|
---|
929 | * @param[in] Im_in pointer to input tensor
|
---|
930 | * @param[in] dim_im_in input tensor dimention
|
---|
931 | * @param[in] ch_im_in number of input tensor channels
|
---|
932 | * @param[in] dim_kernel filter kernel size
|
---|
933 | * @param[in] padding padding sizes
|
---|
934 | * @param[in] stride convolution stride
|
---|
935 | * @param[in] dim_im_out output tensor dimension
|
---|
936 | * @param[in,out] bufferA pointer to buffer space for input
|
---|
937 | * @param[in,out] Im_out pointer to output tensor
|
---|
938 | * @return none.
|
---|
939 | *
|
---|
940 | */
|
---|
941 |
|
---|
942 | void arm_maxpool_q7_HWC(q7_t * Im_in,
|
---|
943 | const uint16_t dim_im_in,
|
---|
944 | const uint16_t ch_im_in,
|
---|
945 | const uint16_t dim_kernel,
|
---|
946 | const uint16_t padding,
|
---|
947 | const uint16_t stride,
|
---|
948 | const uint16_t dim_im_out,
|
---|
949 | q7_t * bufferA,
|
---|
950 | q7_t * Im_out);
|
---|
951 |
|
---|
952 | /**
|
---|
953 | * @brief Q7 average pooling function
|
---|
954 | * @param[in] Im_in pointer to input tensor
|
---|
955 | * @param[in] dim_im_in input tensor dimention
|
---|
956 | * @param[in] ch_im_in number of input tensor channels
|
---|
957 | * @param[in] dim_kernel filter kernel size
|
---|
958 | * @param[in] padding padding sizes
|
---|
959 | * @param[in] stride convolution stride
|
---|
960 | * @param[in] dim_im_out output tensor dimension
|
---|
961 | * @param[in,out] bufferA pointer to buffer space for input
|
---|
962 | * @param[in,out] Im_out pointer to output tensor
|
---|
963 | * @return none.
|
---|
964 | *
|
---|
965 | */
|
---|
966 |
|
---|
967 | void arm_avepool_q7_HWC(q7_t * Im_in,
|
---|
968 | const uint16_t dim_im_in,
|
---|
969 | const uint16_t ch_im_in,
|
---|
970 | const uint16_t dim_kernel,
|
---|
971 | const uint16_t padding,
|
---|
972 | const uint16_t stride,
|
---|
973 | const uint16_t dim_im_out,
|
---|
974 | q7_t * bufferA,
|
---|
975 | q7_t * Im_out);
|
---|
976 |
|
---|
977 | /**
|
---|
978 | * @defgroup Softmax Softmax Functions
|
---|
979 | *
|
---|
980 | * EXP(2) based softmax function
|
---|
981 | *
|
---|
982 | */
|
---|
983 |
|
---|
984 | /**
|
---|
985 | * @brief Q7 softmax function
|
---|
986 | * @param[in] vec_in pointer to input vector
|
---|
987 | * @param[in] dim_vec input vector dimention
|
---|
988 | * @param[out] p_out pointer to output vector
|
---|
989 | * @return none.
|
---|
990 | *
|
---|
991 | */
|
---|
992 |
|
---|
993 | void arm_softmax_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out);
|
---|
994 |
|
---|
995 | /**
|
---|
996 | * @brief Q15 softmax function
|
---|
997 | * @param[in] vec_in pointer to input vector
|
---|
998 | * @param[in] dim_vec input vector dimention
|
---|
999 | * @param[out] p_out pointer to output vector
|
---|
1000 | * @return none.
|
---|
1001 | *
|
---|
1002 | */
|
---|
1003 |
|
---|
1004 | void arm_softmax_q15(const q15_t * vec_in, const uint16_t dim_vec, q15_t * p_out);
|
---|
1005 |
|
---|
1006 | #ifdef __cplusplus
|
---|
1007 | }
|
---|
1008 | #endif
|
---|
1009 |
|
---|
1010 | #endif
|
---|