1 /*
2 * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
3 *
4 * SPDX-License-Identifier: Apache-2.0
5 *
6 * Licensed under the Apache License, Version 2.0 (the License); you may
7 * not use this file except in compliance with the License.
8 * You may obtain a copy of the License at
9 *
10 * www.apache.org/licenses/LICENSE-2.0
11 *
12 * Unless required by applicable law or agreed to in writing, software
13 * distributed under the License is distributed on an AS IS BASIS, WITHOUT
14 * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15 * See the License for the specific language governing permissions and
16 * limitations under the License.
17 */
18
19 /* ----------------------------------------------------------------------
20 * Project: CMSIS NN Library
21 * Title: arm_softmax_q15.c
22 * Description: Q15 softmax function
23 *
24 * $Date: 09. October 2020
25 * $Revision: V.1.0.1
26 *
27 * Target Processor: Cortex-M cores
28 *
29 * -------------------------------------------------------------------- */
30
31 #include "arm_nnfunctions.h"
32
33 /**
34 * @ingroup groupNN
35 */
36
37 /**
38 * @addtogroup Softmax
39 * @{
40 */
41
42 /**
43 * @brief Q15 softmax function
44 * @param[in] vec_in pointer to input vector
45 * @param[in] dim_vec input vector dimention
46 * @param[out] p_out pointer to output vector
47 *
48 * @details
49 *
50 * Here, instead of typical e based softmax, we use
51 * 2-based softmax, i.e.,:
52 *
53 * y_i = 2^(x_i) / sum(2^x_j)
54 *
55 * The relative output will be different here.
56 * But mathematically, the gradient will be the same
57 * with a log(2) scaling factor.
58 *
59 */
60
arm_softmax_q15(const q15_t * vec_in,const uint16_t dim_vec,q15_t * p_out)61 void arm_softmax_q15(const q15_t *vec_in, const uint16_t dim_vec, q15_t *p_out)
62 {
63 q31_t sum;
64 int16_t i;
65 uint8_t shift;
66 q31_t base;
67 base = -1 * 0x100000;
68 for (i = 0; i < dim_vec; i++)
69 {
70 if (vec_in[i] > base)
71 {
72 base = vec_in[i];
73 }
74 }
75
76 /* we ignore really small values
77 * anyway, they will be 0 after shrinking
78 * to q15_t
79 */
80 base = base - 16;
81
82 sum = 0;
83
84 for (i = 0; i < dim_vec; i++)
85 {
86 if (vec_in[i] > base)
87 {
88 shift = (uint8_t)__USAT(vec_in[i] - base, 5);
89 sum += 0x1 << shift;
90 }
91 }
92
93 /* This is effectively (0x1 << 32) / sum */
94 int64_t div_base = 0x100000000LL;
95 int output_base = (int32_t)(div_base / sum);
96
97 /* Final confidence will be output_base >> ( 17 - (vec_in[i] - base) )
98 * so 32768 (0x1<<15) -> 100% confidence when sum = 0x1 << 16, output_base = 0x1 << 16
99 * and vec_in[i]-base = 16
100 */
101 for (i = 0; i < dim_vec; i++)
102 {
103 if (vec_in[i] > base)
104 {
105 /* Here minimum value of 17+base-vec[i] will be 1 */
106 shift = (uint8_t)__USAT(17 + base - vec_in[i], 5);
107 p_out[i] = (q15_t)__SSAT((output_base >> shift), 16);
108 }
109 else
110 {
111 p_out[i] = 0;
112 }
113 }
114 }
115
116 /**
117 * @} end of Softmax group
118 */
119
