1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
|
/*
* Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
*
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the License); you may
* not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an AS IS BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/* ----------------------------------------------------------------------
* Project: CMSIS NN Library
* Title: arm_convolve_HWC_q15_fast.c
* Description: Fast Q15 version of convolution
*
* $Date: 17. January 2018
* $Revision: V.1.0.0
*
* Target Processor: Cortex-M cores
*
* -------------------------------------------------------------------- */
#include "arm_math.h"
#include "arm_nnfunctions.h"
/**
* @ingroup groupNN
*/
/**
* @addtogroup NNConv
* @{
*/
/**
* @brief Fast Q15 convolution function
* @param[in] Im_in pointer to input tensor
* @param[in] dim_im_in input tensor dimention
* @param[in] ch_im_in number of input tensor channels
* @param[in] wt pointer to kernel weights
* @param[in] ch_im_out number of filters, i.e., output tensor channels
* @param[in] dim_kernel filter kernel size
* @param[in] padding padding sizes
* @param[in] stride convolution stride
* @param[in] bias pointer to bias
* @param[in] bias_shift amount of left-shift for bias
* @param[in] out_shift amount of right-shift for output
* @param[in,out] Im_out pointer to output tensor
* @param[in] dim_im_out output tensor dimension
* @param[in,out] bufferA pointer to buffer space for input
* @param[in,out] bufferB pointer to buffer space for output
* @return The function returns either
* <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
*
* @details
*
* <b>Buffer size:</b>
*
* bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
*
* bufferB size: 0
*
* <b>Input dimension constraints:</b>
*
* ch_im_in is multiple of 2
*
* ch_im_out is multipe of 2
*
*/
arm_status
arm_convolve_HWC_q15_fast(const q15_t * Im_in,
const uint16_t dim_im_in,
const uint16_t ch_im_in,
const q15_t * wt,
const uint16_t ch_im_out,
const uint16_t dim_kernel,
const uint16_t padding,
const uint16_t stride,
const q15_t * bias,
const uint16_t bias_shift,
const uint16_t out_shift,
q15_t * Im_out,
const uint16_t dim_im_out,
q15_t * bufferA,
q7_t * bufferB)
{
#if defined (ARM_MATH_DSP)
int16_t i_out_y, i_out_x, i_ker_y, i_ker_x;
q15_t *pBuffer = bufferA;
q15_t *im_buffer = bufferA;
q15_t *pOut = Im_out;
if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0)
{
/* check if the input dimension meets the constraints */
return ARM_MATH_SIZE_MISMATCH;
}
/* Run the following code for Cortex-M4 and Cortex-M7 */
/* This part implements the im2col function */
for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
{
for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
{
for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
{
for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
{
if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
{
/* arm_fill_q15(0, pBuffer, ch_im_in); */
memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
} else
{
/* arm_copy_q15((q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
memcpy(pBuffer, (q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, sizeof(q15_t)*ch_im_in);
}
pBuffer += ch_im_in;
}
}
if (i_out_x & 0x1)
{
int i;
/* initialize the matrix pointers for A */
const q15_t *pA = wt;
/* set up the second output pointers */
q15_t *pOut2 = pOut + ch_im_out;
/* this loop over rows in A */
for (i = 0; i < ch_im_out; i += 2)
{
/* setup pointers for B */
q15_t *pB = im_buffer;
const q15_t *pB2 = pB + ch_im_in * dim_kernel * dim_kernel;
/* aling the second pointer for A */
const q15_t *pA2 = pA + ch_im_in * dim_kernel * dim_kernel;
/* init the sum with bias */
q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
q31_t sum2 = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
q31_t sum3 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift);
q31_t sum4 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift);
uint16_t colCnt = ch_im_in * dim_kernel * dim_kernel >> 1;
/* accumulate over the vector */
while (colCnt)
{
q31_t inA1 = *__SIMD32(pA)++;
q31_t inB1 = *__SIMD32(pB)++;
q31_t inA2 = *__SIMD32(pA2)++;
q31_t inB2 = *__SIMD32(pB2)++;
sum = __SMLAD(inA1, inB1, sum);
sum2 = __SMLAD(inA1, inB2, sum2);
sum3 = __SMLAD(inA2, inB1, sum3);
sum4 = __SMLAD(inA2, inB2, sum4);
colCnt--;
} /* while over colCnt */
colCnt = ch_im_in * dim_kernel * dim_kernel & 0x1;
while (colCnt)
{
q15_t inA1 = *pA++;
q15_t inB1 = *pB++;
q15_t inA2 = *pA2++;
q15_t inB2 = *pB2++;
sum += inA1 * inB1;
sum2 += inA1 * inB2;
sum3 += inA2 * inB1;
sum4 += inA2 * inB2;
colCnt--;
} /* while over colCnt */
*pOut++ = (q15_t) __SSAT(sum >> out_shift, 16);
*pOut++ = (q15_t) __SSAT(sum3 >> out_shift, 16);
*pOut2++ = (q15_t) __SSAT(sum2 >> out_shift, 16);
*pOut2++ = (q15_t) __SSAT(sum4 >> out_shift, 16);
/* skip the row computed with A2 */
pA += ch_im_in * dim_kernel * dim_kernel;
} /* for over ch_im_out */
pOut += ch_im_out;
/* counter reset */
pBuffer = im_buffer;
}
}
}
#else
/* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
uint16_t i, j, k, l, m, n;
int conv_out;
signed char in_row, in_col;
if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0)
{
/* check if the input dimension meets the constraints */
return ARM_MATH_SIZE_MISMATCH;
}
for (i = 0; i < ch_im_out; i++)
{
for (j = 0; j < dim_im_out; j++)
{
for (k = 0; k < dim_im_out; k++)
{
conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
for (m = 0; m < dim_kernel; m++)
{
for (n = 0; n < dim_kernel; n++)
{
in_row = stride * j + m - padding;
in_col = stride * k + n - padding;
if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
{
for (l = 0; l < ch_im_in; l++)
{
conv_out +=
Im_in[(in_row * dim_im_in + in_col) * ch_im_in +
l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel +
n) * ch_im_in + l];
}
}
}
}
Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16);
}
}
}
#endif /* ARM_MATH_DSP */
/* Return to application */
return ARM_MATH_SUCCESS;
}
/**
* @} end of NNConv group
*/
|
