| 1 | /*
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| 2 | * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
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| 3 | *
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| 4 | * SPDX-License-Identifier: Apache-2.0
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| 5 | *
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| 6 | * Licensed under the Apache License, Version 2.0 (the License); you may
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| 7 | * not use this file except in compliance with the License.
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| 8 | * You may obtain a copy of the License at
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| 9 | *
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| 10 | * www.apache.org/licenses/LICENSE-2.0
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| 11 | *
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| 12 | * Unless required by applicable law or agreed to in writing, software
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| 13 | * distributed under the License is distributed on an AS IS BASIS, WITHOUT
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| 14 | * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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| 15 | * See the License for the specific language governing permissions and
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| 16 | * limitations under the License.
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| 17 | */
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| 18 |
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| 19 | /* ----------------------------------------------------------------------
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| 20 | * Project: CMSIS NN Library
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| 21 | * Title: arm_softmax_q7.c
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| 22 | * Description: Q7 softmax function
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| 23 | *
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| 24 | * $Date: 20. February 2018
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| 25 | * $Revision: V.1.0.0
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| 26 | *
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| 27 | * Target Processor: Cortex-M cores
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| 28 | *
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| 29 | * -------------------------------------------------------------------- */
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| 30 |
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| 31 | #include "arm_math.h"
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| 32 | #include "arm_nnfunctions.h"
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| 33 |
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| 34 | /**
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| 35 | * @ingroup groupNN
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| 36 | */
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| 37 |
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| 38 | /**
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| 39 | * @addtogroup Softmax
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| 40 | * @{
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| 41 | */
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| 42 |
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| 43 | /**
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| 44 | * @brief Q7 softmax function
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| 45 | * @param[in] vec_in pointer to input vector
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| 46 | * @param[in] dim_vec input vector dimention
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| 47 | * @param[out] p_out pointer to output vector
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| 48 | * @return none.
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| 49 | *
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| 50 | * @details
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| 51 | *
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| 52 | * Here, instead of typical natural logarithm e based softmax, we use
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| 53 | * 2-based softmax here, i.e.,:
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| 54 | *
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| 55 | * y_i = 2^(x_i) / sum(2^x_j)
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| 56 | *
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| 57 | * The relative output will be different here.
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| 58 | * But mathematically, the gradient will be the same
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| 59 | * with a log(2) scaling factor.
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| 60 | *
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| 61 | */
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| 62 |
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| 63 | void arm_softmax_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out)
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| 64 | {
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| 65 | q31_t sum;
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| 66 | int16_t i;
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| 67 | uint8_t shift;
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| 68 | q15_t base;
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| 69 | base = -257;
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| 70 |
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| 71 | /* We first search for the maximum */
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| 72 | for (i = 0; i < dim_vec; i++)
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| 73 | {
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| 74 | if (vec_in[i] > base)
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| 75 | {
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| 76 | base = vec_in[i];
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| 77 | }
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| 78 | }
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| 79 |
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| 80 | /*
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| 81 | * So the base is set to max-8, meaning
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| 82 | * that we ignore really small values.
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| 83 | * anyway, they will be 0 after shrinking to q7_t.
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| 84 | */
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| 85 | base = base - 8;
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| 86 |
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| 87 | sum = 0;
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| 88 |
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| 89 | for (i = 0; i < dim_vec; i++)
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| 90 | {
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| 91 | if (vec_in[i] > base)
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| 92 | {
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| 93 | shift = (uint8_t)__USAT(vec_in[i] - base, 5);
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| 94 | sum += 0x1 << shift;
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| 95 | }
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| 96 | }
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| 97 |
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| 98 | /* This is effectively (0x1 << 20) / sum */
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| 99 | int output_base = 0x100000 / sum;
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| 100 |
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| 101 | /*
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| 102 | * Final confidence will be output_base >> ( 13 - (vec_in[i] - base) )
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| 103 | * so 128 (0x1<<7) -> 100% confidence when sum = 0x1 << 8, output_base = 0x1 << 12
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| 104 | * and vec_in[i]-base = 8
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| 105 | */
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| 106 | for (i = 0; i < dim_vec; i++)
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| 107 | {
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| 108 | if (vec_in[i] > base)
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| 109 | {
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| 110 | /* Here minimum value of 13+base-vec_in[i] will be 5 */
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| 111 | shift = (uint8_t)__USAT(13+base-vec_in[i], 5);
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| 112 | p_out[i] = (q7_t) __SSAT((output_base >> shift), 8);
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| 113 | } else {
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| 114 | p_out[i] = 0;
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| 115 | }
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| 116 | }
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| 117 | }
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| 118 |
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| 119 | /**
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| 120 | * @} end of Softmax group
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| 121 | */
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