diff --git a/src/cmsis/include/arm_nnfunctions.h b/src/cmsis/include/arm_nnfunctions.h index c4015eca4..366ece529 100644 --- a/src/cmsis/include/arm_nnfunctions.h +++ b/src/cmsis/include/arm_nnfunctions.h @@ -21,7 +21,7 @@ * Title: arm_nnfunctions.h * Description: Public header file for CMSIS NN Library * - * $Date: 17. January 2018 + * $Date: 13. July 2018 * $Revision: V.1.0.0 * * Target Processor: Cortex-M cores @@ -158,6 +158,52 @@ extern "C" q15_t * bufferA, q7_t * bufferB); + /** + * @brief Basic Q7 convolution function (non-sqaure shape) + * @param[in] Im_in pointer to input tensor + * @param[in] dim_im_in_x input tensor dimention x + * @param[in] dim_im_in_y input tensor dimention y + * @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_x filter kernel size x + * @param[in] dim_kernel_y filter kernel size y + * @param[in] padding_x padding size x + * @param[in] padding_y padding size y + * @param[in] stride_x convolution stride x + * @param[in] stride_y convolution stride y + * @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_x output tensor dimension x + * @param[in] dim_im_out_y output tensor dimension y + * @param[in,out] bufferA pointer to buffer space for input + * @param[in,out] bufferB pointer to buffer space for output + * @return The function returns ARM_MATH_SUCCESS + */ + + arm_status arm_convolve_HWC_q7_basic_nonsquare(const q7_t * Im_in, + const uint16_t dim_im_in_x, + const uint16_t dim_im_in_y, + const uint16_t ch_im_in, + const q7_t * wt, + const uint16_t ch_im_out, + const uint16_t dim_kernel_x, + const uint16_t dim_kernel_y, + const uint16_t padding_x, + const uint16_t padding_y, + const uint16_t stride_x, + const uint16_t stride_y, + const q7_t * bias, + const uint16_t bias_shift, + const uint16_t out_shift, + q7_t * Im_out, + const uint16_t dim_im_out_x, + const uint16_t dim_im_out_y, + q15_t * bufferA, + q7_t * bufferB); + /** * @brief Basic Q15 convolution function * @param[in] Im_in pointer to input tensor diff --git a/src/cmsis/include/arm_nnsupportfunctions.h b/src/cmsis/include/arm_nnsupportfunctions.h index 88cb5367c..846019042 100644 --- a/src/cmsis/include/arm_nnsupportfunctions.h +++ b/src/cmsis/include/arm_nnsupportfunctions.h @@ -21,7 +21,7 @@ * Title: arm_nnsupportfunctions.h * Description: Public header file of support functions for CMSIS NN Library * - * $Date: 17. January 2018 + * $Date: 13. July 2018 * $Revision: V.1.0.0 * * Target Processor: Cortex-M cores @@ -135,6 +135,57 @@ __STATIC_FORCEINLINE void *read_and_pad_reordered(void *source, q31_t * out1, q3 } #endif +/** + * @defgroup NNBasicMath Basic Math Functions for Neural Network Computation + * + * Basic Math Functions for Neural Network Computation + * + */ + +/** + * @brief Q7 vector multiplication with variable output shifts + * @param[in] *pSrcA pointer to the first input vector + * @param[in] *pSrcB pointer to the second input vector + * @param[out] *pDst pointer to the output vector + * @param[in] out_shift amount of right-shift for output + * @param[in] blockSize number of samples in each vector + * @return none. + * + * Scaling and Overflow Behavior: + * \par + * The function uses saturating arithmetic. + * Results outside of the allowable Q15 range [0x8000 0x7FFF] will be saturated. + */ + +void arm_nn_mult_q15( + q15_t * pSrcA, + q15_t * pSrcB, + q15_t * pDst, + const uint16_t out_shift, + uint32_t blockSize); + +/** + * @brief Q7 vector multiplication with variable output shifts + * @param[in] *pSrcA pointer to the first input vector + * @param[in] *pSrcB pointer to the second input vector + * @param[out] *pDst pointer to the output vector + * @param[in] out_shift amount of right-shift for output + * @param[in] blockSize number of samples in each vector + * @return none. + * + * Scaling and Overflow Behavior: + * \par + * The function uses saturating arithmetic. + * Results outside of the allowable Q7 range [0x80 0x7F] will be saturated. + */ + +void arm_nn_mult_q7( + q7_t * pSrcA, + q7_t * pSrcB, + q7_t * pDst, + const uint16_t out_shift, + uint32_t blockSize); + /** * @brief defition to adding rouding offset */ diff --git a/src/cmsis/src/nn/ConvolutionFunctions/arm_convolve_HWC_q7_basic_nonsquare.c b/src/cmsis/src/nn/ConvolutionFunctions/arm_convolve_HWC_q7_basic_nonsquare.c new file mode 100644 index 000000000..b426b924f --- /dev/null +++ b/src/cmsis/src/nn/ConvolutionFunctions/arm_convolve_HWC_q7_basic_nonsquare.c @@ -0,0 +1,228 @@ +/* + * 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_q7_basic.c + * Description: Q7 version of convolution + * + * $Date: 13. July 2018 + * $Revision: V.1.0.0 + * + * Target Processor: Cortex-M cores + * + * -------------------------------------------------------------------- */ +#include "arm_math.h" +#include "arm_nnfunctions.h" + +/** + * @ingroup groupNN + */ + +/** + * @addtogroup NNConv + * @{ + */ + + /** + * @brief Basic Q7 convolution function (non-sqaure shape) + * @param[in] Im_in pointer to input tensor + * @param[in] dim_im_in_x input tensor dimention x + * @param[in] dim_im_in_y input tensor dimention y + * @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_x filter kernel size x + * @param[in] dim_kernel_y filter kernel size y + * @param[in] padding_x padding size x + * @param[in] padding_y padding size y + * @param[in] stride_x convolution stride x + * @param[in] stride_y convolution stride y + * @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_x output tensor dimension x + * @param[in] dim_im_out_y output tensor dimension y + * @param[in,out] bufferA pointer to buffer space for input + * @param[in,out] bufferB pointer to buffer space for output + * @return The function returns ARM_MATH_SUCCESS + */ + +arm_status arm_convolve_HWC_q7_basic_nonsquare(const q7_t * Im_in, + const uint16_t dim_im_in_x, + const uint16_t dim_im_in_y, + const uint16_t ch_im_in, + const q7_t * wt, + const uint16_t ch_im_out, + const uint16_t dim_kernel_x, + const uint16_t dim_kernel_y, + const uint16_t padding_x, + const uint16_t padding_y, + const uint16_t stride_x, + const uint16_t stride_y, + const q7_t * bias, + const uint16_t bias_shift, + const uint16_t out_shift, + q7_t * Im_out, + const uint16_t dim_im_out_x, + const uint16_t dim_im_out_y, + q15_t * bufferA, + q7_t * bufferB) +{ + +#if defined (ARM_MATH_DSP) + /* Run the following code for Cortex-M4 and Cortex-M7 */ + + int16_t i_out_y, i_out_x, i_ker_y, i_ker_x; + + /* + * Here we use bufferA as q15_t internally as computation are done with q15_t level + * im2col are done to output in q15_t format from q7_t input + */ + q15_t *pBuffer = bufferA; + q7_t *pOut = Im_out; + + /* This part implements the im2col function */ + for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++) + { + for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++) + { + for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; i_ker_y++) + { + for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; i_ker_x++) + { + if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x) + { + /* Filling 0 for out-of-bound paddings */ + /* arm_fill_q15(0, pBuffer, ch_im_in); */ + memset(pBuffer, 0, sizeof(q15_t)*ch_im_in); + } else + { + /* Copying the pixel data to column */ + arm_q7_to_q15_no_shift((q7_t *) + Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, pBuffer, ch_im_in); + } + pBuffer += ch_im_in; + } + } + + /* Computation is filed for every 2 columns */ + if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_y * dim_kernel_x) + { + pOut = + arm_nn_mat_mult_kernel_q7_q15(wt, bufferA, + ch_im_out, + ch_im_in * + dim_kernel_y * dim_kernel_x, bias_shift, out_shift, bias, pOut); + + /* counter reset */ + pBuffer = bufferA; + } + } + } + + /* left-over because odd number of output pixels */ + if (pBuffer != bufferA) + { + const q7_t *pA = wt; + int i; + + for (i = 0; i < ch_im_out; i++) + { + /* Load the accumulator with bias first */ + q31_t sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); + + /* Point to the beging of the im2col buffer */ + q15_t *pB = bufferA; + + /* Each time it process 4 entries */ + uint16_t colCnt = ch_im_in * dim_kernel_y * dim_kernel_x >> 2; + + while (colCnt) + { + q31_t inA1, inA2; + q31_t inB1, inB2; + + pA = (q7_t *) read_and_pad((void *)pA, &inA1, &inA2); + + inB1 = *__SIMD32(pB)++; + sum = __SMLAD(inA1, inB1, sum); + inB2 = *__SIMD32(pB)++; + sum = __SMLAD(inA2, inB2, sum); + + colCnt--; + } + colCnt = ch_im_in * dim_kernel_y * dim_kernel_x & 0x3; + while (colCnt) + { + q7_t inA1 = *pA++; + q15_t inB1 = *pB++; + sum += inA1 * inB1; + colCnt--; + } + *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8); + } + } +#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; + + for (i = 0; i < ch_im_out; i++) + { + for (j = 0; j < dim_im_out_y; j++) + { + for (k = 0; k < dim_im_out_x; k++) + { + conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift); + for (m = 0; m < dim_kernel_y; m++) + { + for (n = 0; n < dim_kernel_x; n++) + { + // if-for implementation + in_row = stride_y * j + m - padding_y; + in_col = stride_x * k + n - padding_x; + if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x) + { + for (l = 0; l < ch_im_in; l++) + { + conv_out += + Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] * + wt[i * ch_im_in * dim_kernel_y * dim_kernel_x + + (m * dim_kernel_x + n) * ch_im_in + l]; + } + } + } + } + Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8); + } + } + } + +#endif /* ARM_MATH_DSP */ + + /* Return to application */ + return ARM_MATH_SUCCESS; +} + +/** + * @} end of NNConv group + */ diff --git a/src/cmsis/src/nn/NNSupportFunctions/arm_nn_mult_q15.c b/src/cmsis/src/nn/NNSupportFunctions/arm_nn_mult_q15.c new file mode 100644 index 000000000..5a6045963 --- /dev/null +++ b/src/cmsis/src/nn/NNSupportFunctions/arm_nn_mult_q15.c @@ -0,0 +1,147 @@ +/* + * 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_nn_mult_q15.c + * Description: Q15 vector multiplication with variable output shifts + * + * $Date: 13. July 2018 + * $Revision: V.1.0.0 + * + * Target Processor: Cortex-M cores + * + * -------------------------------------------------------------------- */ + +#include "arm_nnfunctions.h" + +/** + * @ingroup groupSupport + */ + +/** + * @addtogroup NNBasicMath + * @{ + */ + + +/** + * @brief Q7 vector multiplication with variable output shifts + * @param[in] *pSrcA pointer to the first input vector + * @param[in] *pSrcB pointer to the second input vector + * @param[out] *pDst pointer to the output vector + * @param[in] out_shift amount of right-shift for output + * @param[in] blockSize number of samples in each vector + * @return none. + * + * Scaling and Overflow Behavior: + * \par + * The function uses saturating arithmetic. + * Results outside of the allowable Q15 range [0x8000 0x7FFF] will be saturated. + */ + +void arm_nn_mult_q15( + q15_t * pSrcA, + q15_t * pSrcB, + q15_t * pDst, + const uint16_t out_shift, + uint32_t blockSize) +{ + uint32_t blkCnt; /* loop counters */ + +#if defined (ARM_MATH_DSP) + +/* Run the below code for Cortex-M4 and Cortex-M3 */ + q31_t inA1, inA2, inB1, inB2; /* temporary input variables */ + q15_t out1, out2, out3, out4; /* temporary output variables */ + q31_t mul1, mul2, mul3, mul4; /* temporary variables */ + + /* loop Unrolling */ + blkCnt = blockSize >> 2U; + + /* First part of the processing with loop unrolling. Compute 4 outputs at a time. + ** a second loop below computes the remaining 1 to 3 samples. */ + while (blkCnt > 0U) + { + /* read two samples at a time from sourceA */ + inA1 = *__SIMD32(pSrcA)++; + /* read two samples at a time from sourceB */ + inB1 = *__SIMD32(pSrcB)++; + /* read two samples at a time from sourceA */ + inA2 = *__SIMD32(pSrcA)++; + /* read two samples at a time from sourceB */ + inB2 = *__SIMD32(pSrcB)++; + + /* multiply mul = sourceA * sourceB */ + mul1 = (q31_t) ((q15_t) (inA1 >> 16) * (q15_t) (inB1 >> 16)); + mul2 = (q31_t) ((q15_t) inA1 * (q15_t) inB1); + mul3 = (q31_t) ((q15_t) (inA2 >> 16) * (q15_t) (inB2 >> 16)); + mul4 = (q31_t) ((q15_t) inA2 * (q15_t) inB2); + + /* saturate result to 16 bit */ + out1 = (q15_t) __SSAT((mul1 + NN_ROUND(out_shift)) >> out_shift, 16); + out2 = (q15_t) __SSAT((mul2 + NN_ROUND(out_shift)) >> out_shift, 16); + out3 = (q15_t) __SSAT((mul3 + NN_ROUND(out_shift)) >> out_shift, 16); + out4 = (q15_t) __SSAT((mul4 + NN_ROUND(out_shift)) >> out_shift, 16); + + /* store the result */ +#ifndef ARM_MATH_BIG_ENDIAN + + *__SIMD32(pDst)++ = __PKHBT(out2, out1, 16); + *__SIMD32(pDst)++ = __PKHBT(out4, out3, 16); + +#else + + *__SIMD32(pDst)++ = __PKHBT(out2, out1, 16); + *__SIMD32(pDst)++ = __PKHBT(out4, out3, 16); + +#endif /* #ifndef ARM_MATH_BIG_ENDIAN */ + + /* Decrement the blockSize loop counter */ + blkCnt--; + } + + /* If the blockSize is not a multiple of 4, compute any remaining output samples here. + ** No loop unrolling is used. */ + blkCnt = blockSize % 0x4U; + +#else + + /* Run the below code for Cortex-M0 */ + + /* Initialize blkCnt with number of samples */ + blkCnt = blockSize; + +#endif /* #if defined (ARM_MATH_DSP) */ + + + while (blkCnt > 0U) + { + /* C = A * B */ + /* Multiply the inputs and store the result in the destination buffer */ + *pDst++ = (q15_t) __SSAT((((q31_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 16); + + /* Decrement the blockSize loop counter */ + blkCnt--; + } +} + +/** + * @} end of NNBasicMath group + */ + diff --git a/src/cmsis/src/nn/NNSupportFunctions/arm_nn_mult_q7.c b/src/cmsis/src/nn/NNSupportFunctions/arm_nn_mult_q7.c new file mode 100644 index 000000000..3735c0499 --- /dev/null +++ b/src/cmsis/src/nn/NNSupportFunctions/arm_nn_mult_q7.c @@ -0,0 +1,119 @@ +/* + * 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_nn_mult_q7.c + * Description: Q7 vector multiplication with variable output shifts + * + * $Date: 13. July 2018 + * $Revision: V.1.0.0 + * + * Target Processor: Cortex-M cores + * + * -------------------------------------------------------------------- */ + +#include "arm_nnfunctions.h" + +/** + * @ingroup groupSupport + */ + +/** + * @addtogroup NNBasicMath + * @{ + */ + +/** + * @brief Q7 vector multiplication with variable output shifts + * @param[in] *pSrcA pointer to the first input vector + * @param[in] *pSrcB pointer to the second input vector + * @param[out] *pDst pointer to the output vector + * @param[in] out_shift amount of right-shift for output + * @param[in] blockSize number of samples in each vector + * @return none. + * + * Scaling and Overflow Behavior: + * \par + * The function uses saturating arithmetic. + * Results outside of the allowable Q7 range [0x80 0x7F] will be saturated. + */ + +void arm_nn_mult_q7( + q7_t * pSrcA, + q7_t * pSrcB, + q7_t * pDst, + const uint16_t out_shift, + uint32_t blockSize) +{ + uint32_t blkCnt; /* loop counters */ + +#if defined (ARM_MATH_DSP) + +/* Run the below code for Cortex-M4 and Cortex-M3 */ + q7_t out1, out2, out3, out4; /* Temporary variables to store the product */ + + /* loop Unrolling */ + blkCnt = blockSize >> 2U; + + /* First part of the processing with loop unrolling. Compute 4 outputs at a time. + ** a second loop below computes the remaining 1 to 3 samples. */ + while (blkCnt > 0U) + { + /* C = A * B */ + /* Multiply the inputs and store the results in temporary variables */ + out1 = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8); + out2 = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8); + out3 = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8); + out4 = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8); + + /* Store the results of 4 inputs in the destination buffer in single cycle by packing */ + *__SIMD32(pDst)++ = __PACKq7(out1, out2, out3, out4); + + /* Decrement the blockSize loop counter */ + blkCnt--; + } + + /* If the blockSize is not a multiple of 4, compute any remaining output samples here. + ** No loop unrolling is used. */ + blkCnt = blockSize % 0x4U; + +#else + + /* Run the below code for Cortex-M0 */ + + /* Initialize blkCnt with number of samples */ + blkCnt = blockSize; + +#endif /* #if defined (ARM_MATH_DSP) */ + + + while (blkCnt > 0U) + { + /* C = A * B */ + /* Multiply the inputs and store the result in the destination buffer */ + *pDst++ = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8); + + /* Decrement the blockSize loop counter */ + blkCnt--; + } +} + +/** + * @} end of NNBasicMath group + */ diff --git a/src/cmsis/src/nn/NNSupportFunctions/arm_q7_to_q15_no_shift.c b/src/cmsis/src/nn/NNSupportFunctions/arm_q7_to_q15_no_shift.c index fa58a2c4f..264e7608e 100644 --- a/src/cmsis/src/nn/NNSupportFunctions/arm_q7_to_q15_no_shift.c +++ b/src/cmsis/src/nn/NNSupportFunctions/arm_q7_to_q15_no_shift.c @@ -130,5 +130,5 @@ void arm_q7_to_q15_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize) } /** - * @} end of q7_to_x group + * @} end of nndata_convert group */ diff --git a/src/cmsis/src/nn/SoftmaxFunctions/arm_softmax_q15.c b/src/cmsis/src/nn/SoftmaxFunctions/arm_softmax_q15.c index 532e886d4..abc27376a 100644 --- a/src/cmsis/src/nn/SoftmaxFunctions/arm_softmax_q15.c +++ b/src/cmsis/src/nn/SoftmaxFunctions/arm_softmax_q15.c @@ -21,7 +21,7 @@ * Title: arm_softmax_q15.c * Description: Q15 softmax function * - * $Date: 17. January 2018 + * $Date: 20. February 2018 * $Revision: V.1.0.0 * * Target Processor: Cortex-M cores @@ -64,41 +64,53 @@ void arm_softmax_q15(const q15_t * vec_in, const uint16_t dim_vec, q15_t * p_out { q31_t sum; int16_t i; - q31_t min, max; - max = -1 * 0x100000; - min = 0x100000; + uint8_t shift; + q31_t base; + base = -1 * 0x100000; for (i = 0; i < dim_vec; i++) { - if (vec_in[i] > max) + if (vec_in[i] > base) { - max = vec_in[i]; - } - if (vec_in[i] < min) - { - min = vec_in[i]; + base = vec_in[i]; } } /* we ignore really small values * anyway, they will be 0 after shrinking - * to q7_t + * to q15_t */ - if (max - min > 16) - { - min = max - 16; - } + base = base - 16; sum = 0; for (i = 0; i < dim_vec; i++) { - sum += 0x1 << (vec_in[i] - min); + if (vec_in[i] > base) + { + shift = (uint8_t)__USAT(vec_in[i] - base, 5); + sum += 0x1 << shift; + } } + /* This is effectively (0x1 << 32) / sum */ + int64_t div_base = 0x100000000LL; + int output_base = (int32_t)(div_base / sum); + + /* Final confidence will be output_base >> ( 17 - (vec_in[i] - base) ) + * so 32768 (0x1<<15) -> 100% confidence when sum = 0x1 << 16, output_base = 0x1 << 16 + * and vec_in[i]-base = 16 + */ for (i = 0; i < dim_vec; i++) { - /* we leave 7-bit dynamic range, so that 128 -> 100% confidence */ - p_out[i] = (q15_t) __SSAT(((0x1 << (vec_in[i] - min + 14)) / sum), 16); + if (vec_in[i] > base) + { + /* Here minimum value of 17+base-vec[i] will be 1 */ + shift = (uint8_t)__USAT(17+base-vec_in[i], 5); + p_out[i] = (q15_t) __SSAT((output_base >> shift), 16); + } else + { + p_out[i] = 0; + } } }