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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_fully_connected_q15_opt.c |
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22 | * Description: Q15 opt fully-connected layer function |
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23 | * |
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24 | * $Date: 17. January 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 | |||
31 | #include "arm_math.h" |
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32 | #include "arm_nnfunctions.h" |
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33 | |||
34 | /** |
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35 | * @ingroup groupNN |
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36 | */ |
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37 | |||
38 | /** |
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39 | * @addtogroup FC |
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40 | * @{ |
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41 | */ |
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42 | |||
43 | /** |
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44 | * @brief Q15 opt fully-connected layer function |
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45 | * @param[in] pV pointer to input vector |
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46 | * @param[in] pM pointer to matrix weights |
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47 | * @param[in] dim_vec length of the vector |
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48 | * @param[in] num_of_rows number of rows in weight matrix |
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49 | * @param[in] bias_shift amount of left-shift for bias |
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50 | * @param[in] out_shift amount of right-shift for output |
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51 | * @param[in] bias pointer to bias |
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52 | * @param[in,out] pOut pointer to output vector |
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53 | * @param[in,out] vec_buffer pointer to buffer space for input |
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54 | * @return The function returns <code>ARM_MATH_SUCCESS</code> |
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55 | * |
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56 | * |
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57 | * @details |
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58 | * |
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59 | * <b>Buffer size:</b> |
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60 | * |
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61 | * vec_buffer size: 0 |
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62 | * |
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63 | * Here we use only one pointer to read 4 rows in the weight |
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64 | * matrix. So if the original matrix looks like this: |
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65 | * |
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66 | * | a11 | a12 | a13 | |
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67 | * |
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68 | * | a21 | a22 | a23 | |
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69 | * |
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70 | * | a31 | a32 | a33 | |
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71 | * |
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72 | * | a41 | a42 | a43 | |
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73 | * |
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74 | * | a51 | a52 | a53 | |
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75 | * |
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76 | * | a61 | a62 | a63 | |
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77 | * |
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78 | * We operates on multiple-of-4 rows, so the first four rows becomes |
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79 | * |
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80 | * | a11 | a12 | a21 | a22 | a31 | a32 | a41 | a42 | |
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81 | * |
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82 | * | a13 | a23 | a33 | a43 | |
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83 | * |
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84 | * Remaining rows are kept the same original order. |
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85 | * |
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86 | * So the stored weight matrix looks like this: |
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87 | * |
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88 | * |
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89 | * | a11 | a12 | a21 | a22 | a31 | a32 | a41 | a42 | |
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90 | * |
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91 | * | a13 | a23 | a33 | a43 | a51 | a52 | a53 | a61 | |
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92 | * |
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93 | * | a62 | a63 | |
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94 | */ |
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95 | |||
96 | arm_status |
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97 | arm_fully_connected_q15_opt(const q15_t * pV, |
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98 | const q15_t * pM, |
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99 | const uint16_t dim_vec, |
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100 | const uint16_t num_of_rows, |
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101 | const uint16_t bias_shift, |
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102 | const uint16_t out_shift, |
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103 | const q15_t * bias, |
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104 | q15_t * pOut, |
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105 | q15_t * vec_buffer) |
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106 | { |
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107 | |||
108 | #if defined (ARM_MATH_DSP) |
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109 | /* Run the following code for Cortex-M4 and Cortex-M7 */ |
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110 | |||
111 | const q15_t *pB = pM; |
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112 | q15_t *pO = pOut; |
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113 | const q15_t *pBias = bias; |
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114 | const q15_t *pA = pV; |
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115 | |||
116 | uint16_t rowCnt = num_of_rows >> 2; |
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117 | |||
118 | while (rowCnt) |
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119 | { |
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120 | q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); |
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121 | q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); |
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122 | q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); |
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123 | q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); |
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124 | |||
125 | uint16_t colCnt = dim_vec >> 1; |
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126 | |||
127 | pA = pV; |
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128 | |||
129 | #ifdef USE_INTRINSIC |
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130 | |||
131 | while (colCnt) |
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132 | { |
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133 | q31_t inM11, inM12, inM13, inM14; |
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134 | q31_t inV; |
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135 | |||
136 | inV = *__SIMD32(pA)++; |
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137 | inM11 = *__SIMD32(pB)++; |
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138 | sum = __SMLAD(inV, inM11, sum); |
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139 | inM12 = *__SIMD32(pB)++; |
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140 | sum2 = __SMLAD(inV, inM12, sum2); |
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141 | inM13 = *__SIMD32(pB)++; |
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142 | sum3 = __SMLAD(inV, inM13, sum3); |
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143 | inM14 = *__SIMD32(pB)++; |
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144 | sum4 = __SMLAD(inV, inM14, sum4); |
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145 | colCnt--; |
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146 | } |
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147 | |||
148 | #else |
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149 | |||
150 | /* |
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151 | * register needed: |
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152 | * loop counter: colCnt |
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153 | * accumulators: sum, sum2, sum3, sum4 |
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154 | * pointers: pB, pA |
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155 | * weight data: inM11, inM12, inM13, inM14 |
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156 | * activation data: inV |
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157 | */ |
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158 | |||
159 | asm volatile ("COL_LOOP_%=:\n" |
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160 | "ldr.w r4, [%[pA]], #4\n" |
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161 | "ldr.w r0, [%[pB]], #16\n" |
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162 | "smlad %[sum], r4, r0, %[sum]\n" |
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163 | "ldr.w r1, [%[pB] , #-12]\n" |
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164 | "smlad %[sum2], r4, r1, %[sum2]\n" |
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165 | "ldr.w r2, [%[pB] , #-8]\n" |
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166 | "smlad %[sum3], r4, r2, %[sum3]\n" |
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167 | "ldr.w r3, [%[pB] , #-4]\n" |
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168 | "smlad %[sum4], r4, r3, %[sum4]\n" |
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169 | "subs %[colCnt], #1\n" |
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170 | "bne COL_LOOP_%=\n":[sum] "+r"(sum), |
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171 | [sum2] "+r"(sum2),[sum3] "+r"(sum3), |
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172 | [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4"); |
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173 | |||
174 | #endif /* USE_INTRINSIC */ |
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175 | |||
176 | colCnt = dim_vec & 0x1; |
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177 | while (colCnt) |
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178 | { |
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179 | |||
180 | q15_t inV = *pA++; |
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181 | q15_t inM = *pB++; |
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182 | q15_t inM2 = *pB++; |
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183 | q15_t inM3 = *pB++; |
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184 | q15_t inM4 = *pB++; |
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185 | |||
186 | sum += inV * inM; |
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187 | sum2 += inV * inM2; |
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188 | sum3 += inV * inM3; |
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189 | sum4 += inV * inM4; |
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190 | colCnt--; |
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191 | } /* while over colCnt */ |
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192 | *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16)); |
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193 | *pO++ = (q15_t) (__SSAT((sum2 >> out_shift), 16)); |
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194 | *pO++ = (q15_t) (__SSAT((sum3 >> out_shift), 16)); |
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195 | *pO++ = (q15_t) (__SSAT((sum4 >> out_shift), 16)); |
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196 | |||
197 | /* adjust the pointers and counters */ |
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198 | rowCnt--; |
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199 | } |
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200 | |||
201 | /* left-over part of the rows */ |
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202 | rowCnt = num_of_rows & 0x3; |
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203 | |||
204 | while (rowCnt) |
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205 | { |
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206 | q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); |
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207 | |||
208 | uint16_t colCnt = dim_vec >> 2; |
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209 | |||
210 | pA = pV; |
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211 | |||
212 | while (colCnt) |
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213 | { |
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214 | q31_t inV1, inV2, inM1, inM2; |
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215 | |||
216 | inM1 = *__SIMD32(pB)++; |
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217 | inV1 = *__SIMD32(pA)++; |
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218 | sum = __SMLAD(inV1, inM1, sum); |
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219 | |||
220 | inM2 = *__SIMD32(pB)++; |
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221 | inV2 = *__SIMD32(pA)++; |
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222 | sum = __SMLAD(inV2, inM2, sum); |
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223 | |||
224 | colCnt--; |
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225 | } |
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226 | |||
227 | /* left-over of the vector */ |
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228 | colCnt = dim_vec & 0x3; |
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229 | while (colCnt) |
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230 | { |
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231 | q15_t inV = *pA++; |
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232 | q15_t inM = *pB++; |
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233 | sum += inV * inM; |
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234 | colCnt--; |
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235 | } |
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236 | |||
237 | *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16)); |
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238 | |||
239 | rowCnt--; |
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240 | } |
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241 | |||
242 | #else |
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243 | /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */ |
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244 | uint16_t rowCnt = num_of_rows >> 2; |
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245 | const q15_t *pB = pM; |
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246 | const q15_t *pA; |
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247 | q15_t *pO = pOut; |
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248 | const q15_t *pBias = bias; |
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249 | |||
250 | while (rowCnt) |
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251 | { |
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252 | q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); |
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253 | q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); |
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254 | q31_t sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); |
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255 | q31_t sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); |
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256 | |||
257 | uint16_t colCnt = dim_vec >> 1; |
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258 | |||
259 | pA = pV; |
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260 | while (colCnt) |
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261 | { |
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262 | q15_t inA1 = *pA++; |
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263 | q15_t inA2 = *pA++; |
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264 | |||
265 | q15_t inB1 = *pB++; |
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266 | q15_t inB2 = *pB++; |
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267 | sum += inA1 * inB1 + inA2 * inB2; |
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268 | |||
269 | inB1 = *pB++; |
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270 | inB2 = *pB++; |
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271 | sum2 += inA1 * inB1 + inA2 * inB2; |
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272 | |||
273 | inB1 = *pB++; |
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274 | inB2 = *pB++; |
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275 | sum3 += inA1 * inB1 + inA2 * inB2; |
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276 | |||
277 | inB1 = *pB++; |
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278 | inB2 = *pB++; |
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279 | sum4 += inA1 * inB1 + inA2 * inB2; |
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280 | |||
281 | colCnt--; |
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282 | } |
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283 | colCnt = dim_vec & 0x1; |
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284 | while (colCnt) |
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285 | { |
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286 | q15_t inA = *pA++; |
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287 | q15_t inB = *pB++; |
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288 | sum += inA * inB; |
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289 | inB = *pB++; |
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290 | sum2 += inA * inB; |
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291 | inB = *pB++; |
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292 | sum3 += inA * inB; |
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293 | inB = *pB++; |
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294 | sum4 += inA * inB; |
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295 | colCnt--; |
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296 | } |
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297 | *pO++ = (q15_t) __SSAT((sum >> out_shift), 16); |
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298 | *pO++ = (q15_t) __SSAT((sum2 >> out_shift), 16); |
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299 | *pO++ = (q15_t) __SSAT((sum3 >> out_shift), 16); |
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300 | *pO++ = (q15_t) __SSAT((sum4 >> out_shift), 16); |
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301 | |||
302 | rowCnt--; |
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303 | } |
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304 | rowCnt = num_of_rows & 0x3; |
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305 | |||
306 | while (rowCnt) |
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307 | { |
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308 | int ip_out = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); |
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309 | int j; |
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310 | |||
311 | pA = pV; |
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312 | for (j = 0; j < dim_vec; j++) |
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313 | { |
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314 | q15_t inA = *pA++; |
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315 | q15_t inB = *pB++; |
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316 | ip_out += inA * inB; |
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317 | } |
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318 | *pO++ = (q15_t) __SSAT((ip_out >> out_shift), 16); |
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319 | |||
320 | rowCnt--; |
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321 | } |
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322 | |||
323 | #endif /* ARM_MATH_DSP */ |
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324 | |||
325 | /* Return to ARM_MATH_SUCCESS */ |
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326 | return (ARM_MATH_SUCCESS); |
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327 | |||
328 | } |
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329 | |||
330 | /** |
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331 | * @} end of FC group |
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332 | */ |