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Diffstat (limited to 'Drivers/CMSIS/DSP/Source/FilteringFunctions/arm_conv_f32.c')
| -rw-r--r-- | Drivers/CMSIS/DSP/Source/FilteringFunctions/arm_conv_f32.c | 816 |
1 files changed, 816 insertions, 0 deletions
diff --git a/Drivers/CMSIS/DSP/Source/FilteringFunctions/arm_conv_f32.c b/Drivers/CMSIS/DSP/Source/FilteringFunctions/arm_conv_f32.c new file mode 100644 index 0000000..84424ca --- /dev/null +++ b/Drivers/CMSIS/DSP/Source/FilteringFunctions/arm_conv_f32.c @@ -0,0 +1,816 @@ +/* ----------------------------------------------------------------------
+ * Project: CMSIS DSP Library
+ * Title: arm_conv_f32.c
+ * Description: Convolution of floating-point sequences
+ *
+ * $Date: 18. March 2019
+ * $Revision: V1.6.0
+ *
+ * Target Processor: Cortex-M cores
+ * -------------------------------------------------------------------- */
+/*
+ * Copyright (C) 2010-2019 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.
+ */
+
+#include "arm_math.h"
+
+/**
+ @ingroup groupFilters
+ */
+
+/**
+ @defgroup Conv Convolution
+
+ Convolution is a mathematical operation that operates on two finite length vectors to generate a finite length output vector.
+ Convolution is similar to correlation and is frequently used in filtering and data analysis.
+ The CMSIS DSP library contains functions for convolving Q7, Q15, Q31, and floating-point data types.
+ The library also provides fast versions of the Q15 and Q31 functions.
+
+ @par Algorithm
+ Let <code>a[n]</code> and <code>b[n]</code> be sequences of length <code>srcALen</code> and
+ <code>srcBLen</code> samples respectively. Then the convolution
+ <pre>
+ c[n] = a[n] * b[n]
+ </pre>
+ @par
+ is defined as
+ \image html ConvolutionEquation.gif
+ @par
+ Note that <code>c[n]</code> is of length <code>srcALen + srcBLen - 1</code> and is defined over the interval <code>n=0, 1, 2, ..., srcALen + srcBLen - 2</code>.
+ <code>pSrcA</code> points to the first input vector of length <code>srcALen</code> and
+ <code>pSrcB</code> points to the second input vector of length <code>srcBLen</code>.
+ The output result is written to <code>pDst</code> and the calling function must allocate <code>srcALen+srcBLen-1</code> words for the result.
+ @par
+ Conceptually, when two signals <code>a[n]</code> and <code>b[n]</code> are convolved,
+ the signal <code>b[n]</code> slides over <code>a[n]</code>.
+ For each offset \c n, the overlapping portions of a[n] and b[n] are multiplied and summed together.
+ @par
+ Note that convolution is a commutative operation:
+ <pre>
+ a[n] * b[n] = b[n] * a[n].
+ </pre>
+ @par
+ This means that switching the A and B arguments to the convolution functions has no effect.
+
+ @par Fixed-Point Behavior
+ Convolution requires summing up a large number of intermediate products.
+ As such, the Q7, Q15, and Q31 functions run a risk of overflow and saturation.
+ Refer to the function specific documentation below for further details of the particular algorithm used.
+
+ @par Fast Versions
+ Fast versions are supported for Q31 and Q15. Cycles for Fast versions are less compared to Q31 and Q15 of conv and the design requires
+ the input signals should be scaled down to avoid intermediate overflows.
+
+ @par Opt Versions
+ Opt versions are supported for Q15 and Q7. Design uses internal scratch buffer for getting good optimisation.
+ These versions are optimised in cycles and consumes more memory (Scratch memory) compared to Q15 and Q7 versions
+ */
+
+/**
+ @addtogroup Conv
+ @{
+ */
+
+/**
+ @brief Convolution of floating-point sequences.
+ @param[in] pSrcA points to the first input sequence
+ @param[in] srcALen length of the first input sequence
+ @param[in] pSrcB points to the second input sequence
+ @param[in] srcBLen length of the second input sequence
+ @param[out] pDst points to the location where the output result is written. Length srcALen+srcBLen-1.
+ @return none
+ */
+
+void arm_conv_f32(
+ const float32_t * pSrcA,
+ uint32_t srcALen,
+ const float32_t * pSrcB,
+ uint32_t srcBLen,
+ float32_t * pDst)
+{
+
+#if (1)
+//#if !defined(ARM_MATH_CM0_FAMILY)
+
+ const float32_t *pIn1; /* InputA pointer */
+ const float32_t *pIn2; /* InputB pointer */
+ float32_t *pOut = pDst; /* Output pointer */
+ const float32_t *px; /* Intermediate inputA pointer */
+ const float32_t *py; /* Intermediate inputB pointer */
+ const float32_t *pSrc1, *pSrc2; /* Intermediate pointers */
+ float32_t sum; /* Accumulators */
+ uint32_t blockSize1, blockSize2, blockSize3; /* Loop counters */
+ uint32_t j, k, count, blkCnt; /* Loop counters */
+
+#if defined (ARM_MATH_LOOPUNROLL) || defined(ARM_MATH_NEON)
+ float32_t acc0, acc1, acc2, acc3; /* Accumulators */
+ float32_t x0, x1, x2, x3, c0; /* Temporary variables to hold state and coefficient values */
+#endif
+
+ /* The algorithm implementation is based on the lengths of the inputs. */
+ /* srcB is always made to slide across srcA. */
+ /* So srcBLen is always considered as shorter or equal to srcALen */
+ if (srcALen >= srcBLen)
+ {
+ /* Initialization of inputA pointer */
+ pIn1 = pSrcA;
+
+ /* Initialization of inputB pointer */
+ pIn2 = pSrcB;
+ }
+ else
+ {
+ /* Initialization of inputA pointer */
+ pIn1 = pSrcB;
+
+ /* Initialization of inputB pointer */
+ pIn2 = pSrcA;
+
+ /* srcBLen is always considered as shorter or equal to srcALen */
+ j = srcBLen;
+ srcBLen = srcALen;
+ srcALen = j;
+ }
+
+ /* conv(x,y) at n = x[n] * y[0] + x[n-1] * y[1] + x[n-2] * y[2] + ...+ x[n-N+1] * y[N -1] */
+ /* The function is internally
+ * divided into three stages according to the number of multiplications that has to be
+ * taken place between inputA samples and inputB samples. In the first stage of the
+ * algorithm, the multiplications increase by one for every iteration.
+ * In the second stage of the algorithm, srcBLen number of multiplications are done.
+ * In the third stage of the algorithm, the multiplications decrease by one
+ * for every iteration. */
+
+ /* The algorithm is implemented in three stages.
+ The loop counters of each stage is initiated here. */
+ blockSize1 = srcBLen - 1U;
+ blockSize2 = srcALen - (srcBLen - 1U);
+ blockSize3 = blockSize1;
+
+ /* --------------------------
+ * Initializations of stage1
+ * -------------------------*/
+
+ /* sum = x[0] * y[0]
+ * sum = x[0] * y[1] + x[1] * y[0]
+ * ....
+ * sum = x[0] * y[srcBlen - 1] + x[1] * y[srcBlen - 2] +...+ x[srcBLen - 1] * y[0]
+ */
+
+ /* In this stage the MAC operations are increased by 1 for every iteration.
+ The count variable holds the number of MAC operations performed */
+ count = 1U;
+
+ /* Working pointer of inputA */
+ px = pIn1;
+
+ /* Working pointer of inputB */
+ py = pIn2;
+
+
+ /* ------------------------
+ * Stage1 process
+ * ----------------------*/
+#if defined(ARM_MATH_NEON)
+ float32x4_t vec1;
+ float32x4_t vec2;
+ float32x4_t res = vdupq_n_f32(0) ;
+ float32x2_t accum = vdup_n_f32(0);
+#endif /* #if defined(ARM_MATH_NEON) */
+
+ /* The first stage starts here */
+ while (blockSize1 > 0U)
+ {
+ /* Accumulator is made zero for every iteration */
+ sum = 0.0f;
+
+#if defined (ARM_MATH_LOOPUNROLL) || defined(ARM_MATH_NEON)
+ /* Loop unrolling: Compute 4 outputs at a time */
+ k = count >> 2U;
+
+#if defined(ARM_MATH_NEON)
+ res = vdupq_n_f32(0) ;
+ accum = vdup_n_f32(0);
+
+ /* Compute 4 MACs simultaneously. */
+ k = count >> 2U;
+
+ /* First part of the processing. Compute 4 MACs at a time.
+ ** a second loop below computes MACs for the remaining 1 to 3 samples. */
+
+ while (k > 0U)
+ {
+ vec1 = vld1q_f32(px);
+ vec2 = vld1q_f32(py-3);
+ vec2 = vrev64q_f32(vec2);
+ vec2 = vcombine_f32(vget_high_f32(vec2), vget_low_f32(vec2));
+
+ res = vmlaq_f32(res,vec1, vec2);
+
+ /* Increment pointers */
+ px += 4;
+ py -= 4;
+
+ /* Decrement the loop counter */
+ k--;
+ }
+
+ accum = vpadd_f32(vget_low_f32(res), vget_high_f32(res));
+ sum += accum[0] + accum[1];
+
+ /* If the count is not a multiple of 4, compute any remaining MACs here.
+ ** No loop unrolling is used. */
+ k = count & 3;
+#else
+ while (k > 0U)
+ {
+ /* x[0] * y[srcBLen - 1] */
+ sum += *px++ * *py--;
+
+ /* x[1] * y[srcBLen - 2] */
+ sum += *px++ * *py--;
+
+ /* x[2] * y[srcBLen - 3] */
+ sum += *px++ * *py--;
+
+ /* x[3] * y[srcBLen - 4] */
+ sum += *px++ * *py--;
+
+ /* Decrement loop counter */
+ k--;
+ }
+
+ /* Loop unrolling: Compute remaining outputs */
+ k = count % 0x4U;
+
+#endif /* #if defined(ARM_MATH_NEON) */
+
+#else
+ /* Initialize k with number of samples */
+ k = count;
+
+#endif /* #if defined (ARM_MATH_LOOPUNROLL) || defined(ARM_MATH_NEON) */
+
+ while (k > 0U)
+ {
+ /* Perform the multiply-accumulate */
+ sum += *px++ * *py--;
+
+ /* Decrement loop counter */
+ k--;
+ }
+
+ /* Store the result in the accumulator in the destination buffer. */
+ *pOut++ = sum;
+
+ /* Update the inputA and inputB pointers for next MAC calculation */
+ py = pIn2 + count;
+ px = pIn1;
+
+ /* Increment MAC count */
+ count++;
+
+ /* Decrement loop counter */
+ blockSize1--;
+ }
+
+ /* --------------------------
+ * Initializations of stage2
+ * ------------------------*/
+
+ /* sum = x[0] * y[srcBLen-1] + x[1] * y[srcBLen-2] +...+ x[srcBLen-1] * y[0]
+ * sum = x[1] * y[srcBLen-1] + x[2] * y[srcBLen-2] +...+ x[srcBLen] * y[0]
+ * ....
+ * sum = x[srcALen-srcBLen-2] * y[srcBLen-1] + x[srcALen] * y[srcBLen-2] +...+ x[srcALen-1] * y[0]
+ */
+
+ /* Working pointer of inputA */
+ px = pIn1;
+
+ /* Working pointer of inputB */
+ pSrc2 = pIn2 + (srcBLen - 1U);
+ py = pSrc2;
+
+ /* count is index by which the pointer pIn1 to be incremented */
+ count = 0U;
+
+ /* -------------------
+ * Stage2 process
+ * ------------------*/
+
+ /* Stage2 depends on srcBLen as in this stage srcBLen number of MACS are performed.
+ * So, to loop unroll over blockSize2,
+ * srcBLen should be greater than or equal to 4 */
+ if (srcBLen >= 4U)
+ {
+
+#if defined(ARM_MATH_NEON)
+ float32x4_t c;
+ float32x4_t x1v;
+ float32x4_t x2v;
+ uint32x4_t x1v_u;
+ uint32x4_t x2v_u;
+ uint32x4_t x_u;
+ float32x4_t x;
+ float32x4_t res = vdupq_n_f32(0) ;
+#endif /* #if defined(ARM_MATH_NEON) */
+
+#if defined (ARM_MATH_LOOPUNROLL) || defined(ARM_MATH_NEON)
+
+ /* Loop unrolling: Compute 4 outputs at a time */
+ blkCnt = blockSize2 >> 2U;
+
+ while (blkCnt > 0U)
+ {
+ /* Set all accumulators to zero */
+ acc0 = 0.0f;
+ acc1 = 0.0f;
+ acc2 = 0.0f;
+ acc3 = 0.0f;
+
+ /* Apply loop unrolling and compute 4 MACs simultaneously. */
+ k = srcBLen >> 2U;
+
+#if defined(ARM_MATH_NEON)
+ res = vdupq_n_f32(0) ;
+
+ x1v = vld1q_f32(px);
+ x2v = vld1q_f32(px+4);
+
+ do
+ {
+ c = vld1q_f32(py-3);
+
+ px += 4;
+ x = x1v;
+ res = vmlaq_n_f32(res,x,c[3]);
+
+ x = vextq_f32(x1v,x2v,1);
+
+ res = vmlaq_n_f32(res,x,c[2]);
+
+ x = vextq_f32(x1v,x2v,2);
+
+ res = vmlaq_n_f32(res,x,c[1]);
+
+ x = vextq_f32(x1v,x2v,3);
+
+ res = vmlaq_n_f32(res,x,c[0]);
+
+ py -= 4;
+
+ x1v = x2v ;
+ x2v = vld1q_f32(px+4);
+
+ } while (--k);
+
+
+ /* If the srcBLen is not a multiple of 4, compute any remaining MACs here.
+ ** No loop unrolling is used. */
+ k = srcBLen & 0x3;
+
+ x1v = vld1q_f32(px);
+ px += 4;
+
+ while (k > 0U)
+ {
+ /* Read y[srcBLen - 5] sample */
+ c0 = *(py--);
+
+ res = vmlaq_n_f32(res,x1v,c0);
+
+ /* Reuse the present samples for the next MAC */
+ x1v[0] = x1v[1];
+ x1v[1] = x1v[2];
+ x1v[2] = x1v[3];
+
+ x1v[3] = *(px++);
+
+ /* Decrement the loop counter */
+ k--;
+ }
+
+ acc0 = res[0];
+ acc1 = res[1];
+ acc2 = res[2];
+ acc3 = res[3];
+
+#else
+ /* read x[0], x[1], x[2] samples */
+ x0 = *px++;
+ x1 = *px++;
+ x2 = *px++;
+
+ /* First part of the processing with loop unrolling. Compute 4 MACs at a time.
+ ** a second loop below computes MACs for the remaining 1 to 3 samples. */
+ do
+ {
+ /* Read y[srcBLen - 1] sample */
+ c0 = *py--;
+ /* Read x[3] sample */
+ x3 = *(px);
+
+ /* Perform the multiply-accumulate */
+ /* acc0 += x[0] * y[srcBLen - 1] */
+ acc0 += x0 * c0;
+ /* acc1 += x[1] * y[srcBLen - 1] */
+ acc1 += x1 * c0;
+ /* acc2 += x[2] * y[srcBLen - 1] */
+ acc2 += x2 * c0;
+ /* acc3 += x[3] * y[srcBLen - 1] */
+ acc3 += x3 * c0;
+
+ /* Read y[srcBLen - 2] sample */
+ c0 = *py--;
+ /* Read x[4] sample */
+ x0 = *(px + 1U);
+
+ /* Perform the multiply-accumulate */
+ /* acc0 += x[1] * y[srcBLen - 2] */
+ acc0 += x1 * c0;
+ /* acc1 += x[2] * y[srcBLen - 2] */
+ acc1 += x2 * c0;
+ /* acc2 += x[3] * y[srcBLen - 2] */
+ acc2 += x3 * c0;
+ /* acc3 += x[4] * y[srcBLen - 2] */
+ acc3 += x0 * c0;
+
+ /* Read y[srcBLen - 3] sample */
+ c0 = *py--;
+ /* Read x[5] sample */
+ x1 = *(px + 2U);
+
+ /* Perform the multiply-accumulate */
+ /* acc0 += x[2] * y[srcBLen - 3] */
+ acc0 += x2 * c0;
+ /* acc1 += x[3] * y[srcBLen - 2] */
+ acc1 += x3 * c0;
+ /* acc2 += x[4] * y[srcBLen - 2] */
+ acc2 += x0 * c0;
+ /* acc3 += x[5] * y[srcBLen - 2] */
+ acc3 += x1 * c0;
+
+ /* Read y[srcBLen - 4] sample */
+ c0 = *py--;
+ /* Read x[6] sample */
+ x2 = *(px + 3U);
+ px += 4U;
+
+ /* Perform the multiply-accumulate */
+ /* acc0 += x[3] * y[srcBLen - 4] */
+ acc0 += x3 * c0;
+ /* acc1 += x[4] * y[srcBLen - 4] */
+ acc1 += x0 * c0;
+ /* acc2 += x[5] * y[srcBLen - 4] */
+ acc2 += x1 * c0;
+ /* acc3 += x[6] * y[srcBLen - 4] */
+ acc3 += x2 * c0;
+
+ } while (--k);
+
+ /* If the srcBLen is not a multiple of 4, compute any remaining MACs here.
+ ** No loop unrolling is used. */
+ k = srcBLen % 0x4U;
+
+ while (k > 0U)
+ {
+ /* Read y[srcBLen - 5] sample */
+ c0 = *py--;
+ /* Read x[7] sample */
+ x3 = *px++;
+
+ /* Perform the multiply-accumulate */
+ /* acc0 += x[4] * y[srcBLen - 5] */
+ acc0 += x0 * c0;
+ /* acc1 += x[5] * y[srcBLen - 5] */
+ acc1 += x1 * c0;
+ /* acc2 += x[6] * y[srcBLen - 5] */
+ acc2 += x2 * c0;
+ /* acc3 += x[7] * y[srcBLen - 5] */
+ acc3 += x3 * c0;
+
+ /* Reuse the present samples for the next MAC */
+ x0 = x1;
+ x1 = x2;
+ x2 = x3;
+
+ /* Decrement the loop counter */
+ k--;
+ }
+#endif /* #if defined(ARM_MATH_NEON) */
+
+ /* Store the result in the accumulator in the destination buffer. */
+ *pOut++ = acc0;
+ *pOut++ = acc1;
+ *pOut++ = acc2;
+ *pOut++ = acc3;
+
+ /* Increment the pointer pIn1 index, count by 4 */
+ count += 4U;
+
+ /* Update the inputA and inputB pointers for next MAC calculation */
+ px = pIn1 + count;
+ py = pSrc2;
+
+ /* Decrement the loop counter */
+ blkCnt--;
+ }
+
+ /* If the blockSize2 is not a multiple of 4, compute any remaining output samples here.
+ ** No loop unrolling is used. */
+ blkCnt = blockSize2 % 0x4U;
+
+#else
+
+ /* Initialize blkCnt with number of samples */
+ blkCnt = blockSize2;
+
+#endif /* #if defined (ARM_MATH_LOOPUNROLL) || defined (ARM_MATH_NEON)*/
+
+ while (blkCnt > 0U)
+ {
+ /* Accumulator is made zero for every iteration */
+ sum = 0.0f;
+
+#if defined(ARM_MATH_NEON) || defined (ARM_MATH_LOOPUNROLL)
+ /* Loop unrolling: Compute 4 outputs at a time */
+ k = srcBLen >> 2U;
+
+#if defined (ARM_MATH_NEON)
+ float32x4_t res = vdupq_n_f32(0) ;
+ float32x4_t x = vdupq_n_f32(0) ;
+ float32x4_t y = vdupq_n_f32(0) ;
+ float32x2_t accum = vdup_n_f32(0) ;
+
+ /* First part of the processing. Compute 4 MACs at a time.
+ ** a second loop below computes MACs for the remaining 1 to 3 samples. */
+ while (k > 0U)
+ {
+ x = vld1q_f32(px);
+ y = vld1q_f32(py-3);
+
+ y = vrev64q_f32(y);
+ y = vcombine_f32(vget_high_f32(y), vget_low_f32(y));
+
+ res = vmlaq_f32(res,x,y);
+
+ px += 4 ;
+ py -= 4 ;
+
+ /* Decrement the loop counter */
+ k--;
+ }
+
+ accum = vpadd_f32(vget_low_f32(res), vget_high_f32(res));
+ sum += accum[0] + accum[1];
+
+ /* If the srcBLen is not a multiple of 4, compute any remaining MACs here.
+ ** No loop unrolling is used. */
+ k = srcBLen & 0x3U;
+
+#else
+ while (k > 0U)
+ {
+ /* Perform the multiply-accumulate */
+ sum += *px++ * *py--;
+ sum += *px++ * *py--;
+ sum += *px++ * *py--;
+ sum += *px++ * *py--;
+
+ /* Decrement loop counter */
+ k--;
+ }
+
+ /* Loop unrolling: Compute remaining outputs */
+ k = srcBLen % 0x4U;
+
+#endif /* if defined (ARM_MATH_NEON) */
+#else
+ /* Initialize blkCnt with number of samples */
+ k = srcBLen;
+
+#endif /* #if defined(ARM_MATH_NEON) || defined (ARM_MATH_LOOPUNROLL) */
+
+ while (k > 0U)
+ {
+ /* Perform the multiply-accumulate */
+ sum += *px++ * *py--;
+
+ /* Decrement the loop counter */
+ k--;
+ }
+
+ /* Store the result in the accumulator in the destination buffer. */
+ *pOut++ = sum;
+
+ /* Increment the MAC count */
+ count++;
+
+ /* Update the inputA and inputB pointers for next MAC calculation */
+ px = pIn1 + count;
+ py = pSrc2;
+
+ /* Decrement the loop counter */
+ blkCnt--;
+ }
+ }
+ else
+ {
+ /* If the srcBLen is not a multiple of 4,
+ * the blockSize2 loop cannot be unrolled by 4 */
+ blkCnt = blockSize2;
+
+ while (blkCnt > 0U)
+ {
+ /* Accumulator is made zero for every iteration */
+ sum = 0.0f;
+
+ /* srcBLen number of MACS should be performed */
+ k = srcBLen;
+
+ while (k > 0U)
+ {
+ /* Perform the multiply-accumulate */
+ sum += *px++ * *py--;
+
+ /* Decrement the loop counter */
+ k--;
+ }
+
+ /* Store the result in the accumulator in the destination buffer. */
+ *pOut++ = sum;
+
+ /* Increment the MAC count */
+ count++;
+
+ /* Update the inputA and inputB pointers for next MAC calculation */
+ px = pIn1 + count;
+ py = pSrc2;
+
+ /* Decrement the loop counter */
+ blkCnt--;
+ }
+ }
+
+
+ /* --------------------------
+ * Initializations of stage3
+ * -------------------------*/
+
+ /* sum += x[srcALen-srcBLen+1] * y[srcBLen-1] + x[srcALen-srcBLen+2] * y[srcBLen-2] +...+ x[srcALen-1] * y[1]
+ * sum += x[srcALen-srcBLen+2] * y[srcBLen-1] + x[srcALen-srcBLen+3] * y[srcBLen-2] +...+ x[srcALen-1] * y[2]
+ * ....
+ * sum += x[srcALen-2] * y[srcBLen-1] + x[srcALen-1] * y[srcBLen-2]
+ * sum += x[srcALen-1] * y[srcBLen-1]
+ */
+
+ /* In this stage the MAC operations are decreased by 1 for every iteration.
+ The blockSize3 variable holds the number of MAC operations performed */
+
+ /* Working pointer of inputA */
+ pSrc1 = pIn1 + (srcALen - (srcBLen - 1U));
+ px = pSrc1;
+
+ /* Working pointer of inputB */
+ pSrc2 = pIn2 + (srcBLen - 1U);
+ py = pSrc2;
+
+ /* -------------------
+ * Stage3 process
+ * ------------------*/
+ while (blockSize3 > 0U)
+ {
+ /* Accumulator is made zero for every iteration */
+ sum = 0.0f;
+
+#if defined (ARM_MATH_LOOPUNROLL) || defined(ARM_MATH_NEON)
+ /* Loop unrolling: Compute 4 outputs at a time */
+ k = blockSize3 >> 2U;
+
+#if defined(ARM_MATH_NEON)
+ float32x4_t res = vdupq_n_f32(0) ;
+ float32x4_t x = vdupq_n_f32(0) ;
+ float32x4_t y = vdupq_n_f32(0) ;
+ float32x2_t accum = vdup_n_f32(0) ;
+
+ while (k > 0U)
+ {
+ x = vld1q_f32(px);
+ y = vld1q_f32(py-3);
+
+ y = vrev64q_f32(y);
+ y = vcombine_f32(vget_high_f32(y), vget_low_f32(y));
+
+ res = vmlaq_f32(res,x,y);
+
+ px += 4 ;
+ py -= 4 ;
+
+ /* Decrement the loop counter */
+ k--;
+ }
+
+ accum = vpadd_f32(vget_low_f32(res), vget_high_f32(res));
+ sum += accum[0] + accum[1];
+
+#else
+ while (k > 0U)
+ {
+ /* Perform the multiply-accumulate */
+ /* sum += x[srcALen - srcBLen + 1] * y[srcBLen - 1] */
+ sum += *px++ * *py--;
+
+ /* sum += x[srcALen - srcBLen + 2] * y[srcBLen - 2] */
+ sum += *px++ * *py--;
+
+ /* sum += x[srcALen - srcBLen + 3] * y[srcBLen - 3] */
+ sum += *px++ * *py--;
+
+ /* sum += x[srcALen - srcBLen + 4] * y[srcBLen - 4] */
+ sum += *px++ * *py--;
+
+ /* Decrement loop counter */
+ k--;
+ }
+#endif /* #if defined (ARM_MATH_NEON) */
+
+ /* Loop unrolling: Compute remaining outputs */
+ k = blockSize3 % 0x4U;
+#else
+
+ /* Initialize blkCnt with number of samples */
+ k = blockSize3;
+
+#endif /* #if defined (ARM_MATH_NEON) || defined (ARM_MATH_LOOPUNROLL)*/
+
+ while (k > 0U)
+ {
+ /* Perform the multiply-accumulate */
+ /* sum += x[srcALen-1] * y[srcBLen-1] */
+ sum += *px++ * *py--;
+
+ /* Decrement loop counter */
+ k--;
+ }
+
+ /* Store the result in the accumulator in the destination buffer. */
+ *pOut++ = sum;
+
+ /* Update the inputA and inputB pointers for next MAC calculation */
+ px = ++pSrc1;
+ py = pSrc2;
+
+ /* Decrement the loop counter */
+ blockSize3--;
+ }
+
+#else
+/* alternate version for CM0_FAMILY */
+
+ const float32_t *pIn1 = pSrcA; /* InputA pointer */
+ const float32_t *pIn2 = pSrcB; /* InputB pointer */
+ float32_t sum; /* Accumulator */
+ uint32_t i, j; /* Loop counters */
+
+ /* Loop to calculate convolution for output length number of times */
+ for (i = 0U; i < (srcALen + srcBLen - 1U); i++)
+ {
+ /* Initialize sum with zero to carry out MAC operations */
+ sum = 0.0f;
+
+ /* Loop to perform MAC operations according to convolution equation */
+ for (j = 0U; j <= i; j++)
+ {
+ /* Check the array limitations */
+ if (((i - j) < srcBLen) && (j < srcALen))
+ {
+ /* z[i] += x[i-j] * y[j] */
+ sum += ( pIn1[j] * pIn2[i - j]);
+ }
+ }
+
+ /* Store the output in the destination buffer */
+ pDst[i] = sum;
+ }
+
+#endif /* #if !defined(ARM_MATH_CM0_FAMILY) */
+
+}
+
+/**
+ @} end of Conv group
+ */
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