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| 2 | mjames | 1 | /* |
| 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 | |||
| 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 | |||
| 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 | |||
| 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 | |||
| 92 | #ifndef _ARM_NNFUNCTIONS_H |
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| 93 | #define _ARM_NNFUNCTIONS_H |
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| 94 | |||
| 95 | #include "arm_nnsupportfunctions.h" |
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| 96 | #include "arm_nn_tables.h" |
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| 97 | |||
| 98 | #define USE_INTRINSIC |
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| 99 | |||
| 100 | //#define ARM_NN_TRUNCATE /* This config the rounding model to floor or round to the nearest int */ |
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| 101 | |||
| 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 | |||
| 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 | |||
| 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 | |||
| 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 | |||
| 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 | |||
| 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 | |||
| 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 | |||
| 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 | |||
| 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 | |||
| 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 | |||
| 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 | |||
| 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 | |||
| 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 | |||
| 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 |
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| 406 | * @param[in,out] Im_out pointer to output tensor |
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| 407 | * @param[in] dim_im_out output tensor dimension |
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| 408 | * @param[in,out] bufferA pointer to buffer space for input |
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| 409 | * @param[in,out] bufferB pointer to buffer space for output |
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| 410 | * @return The function returns either |
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| 411 | * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking. |
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| 412 | * |
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| 413 | * This kernel is written exclusively for convolution with ch_im_in |
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| 414 | * equals 3. This applies on the first layer of CNNs which has input |
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| 415 | * image with RGB format. |
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| 416 | */ |
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| 417 | |||
| 418 | arm_status arm_convolve_HWC_q7_RGB(const q7_t * Im_in, |
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| 419 | const uint16_t dim_im_in, |
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| 420 | const uint16_t ch_im_in, |
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| 421 | const q7_t * wt, |
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| 422 | const uint16_t ch_im_out, |
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| 423 | const uint16_t dim_kernel, |
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| 424 | const uint16_t padding, |
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| 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 |
||
| 801 | * |
||
| 802 | * These functions are used within convolution layer functions for |
||
| 803 | * matrix multiplication. |
||
| 804 | * |
||
| 805 | * The implementation is similar to CMSIS-DSP arm_mat_mult functions |
||
| 806 | * with one Q7 and one Q15 operands. The Q15 operand is the im2col |
||
| 807 | * output which is always with 2 columns. |
||
| 808 | * |
||
| 809 | */ |
||
| 810 | |||
| 811 | /** |
||
| 812 | * @brief Matrix-multiplication function for convolution |
||
| 813 | * @param[in] pA pointer to operand A |
||
| 814 | * @param[in] pInBuffer pointer to operand B, always conssists of 2 vectors |
||
| 815 | * @param[in] ch_im_out numRow of A |
||
| 816 | * @param[in] numCol_A numCol of A |
||
| 817 | * @param[in] bias_shift amount of left-shift for bias |
||
| 818 | * @param[in] out_shift amount of right-shift for output |
||
| 819 | * @param[in] bias the bias |
||
| 820 | * @param[in,out] pOut pointer to output |
||
| 821 | * @return The function returns the incremented output pointer |
||
| 822 | */ |
||
| 823 | |||
| 824 | q7_t *arm_nn_mat_mult_kernel_q7_q15(const q7_t * pA, |
||
| 825 | const q15_t * pInBuffer, |
||
| 826 | const uint16_t ch_im_out, |
||
| 827 | const uint16_t numCol_A, |
||
| 828 | const uint16_t bias_shift, |
||
| 829 | const uint16_t out_shift, |
||
| 830 | const q7_t * bias, |
||
| 831 | q7_t * pOut); |
||
| 832 | |||
| 833 | /** |
||
| 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 | |||
| 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 |
||
| 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 |