diff options
Diffstat (limited to 'fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c')
| -rw-r--r-- | fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c | 255 |
1 files changed, 255 insertions, 0 deletions
diff --git a/fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c b/fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c new file mode 100644 index 0000000..a02aaa0 --- /dev/null +++ b/fw/midi-dials/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c @@ -0,0 +1,255 @@ +/*
+ * 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
+ */
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