/* ---------------------------------------------------------------------------- * Copyright (C) 2010-2013 ARM Limited. All rights reserved. * * $Date: 17. January 2013 * $Revision: V1.4.1 * * Project: CMSIS DSP Library * Title: arm_conv_f32.c * * Description: Convolution of floating-point sequences. * * Target Processor: Cortex-M4/Cortex-M3/Cortex-M0 * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * - Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * - Redistributions in binary form must reproduce the above copyright * notice, this list of conditions and the following disclaimer in * the documentation and/or other materials provided with the * distribution. * - Neither the name of ARM LIMITED nor the names of its contributors * may be used to endorse or promote products derived from this * software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. * -------------------------------------------------------------------------- */ #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 on Cortex-M4 and Cortex-M3. * * \par Algorithm * Let a[n] and b[n] be sequences of length srcALen and srcBLen samples respectively. * Then the convolution * * 
    
 *                   c[n] = a[n] * b[n]    
 *
* * \par * is defined as * \image html ConvolutionEquation.gif * \par * Note that c[n] is of length srcALen + srcBLen - 1 and is defined over the interval n=0, 1, 2, ..., srcALen + srcBLen - 2. * pSrcA points to the first input vector of length srcALen and * pSrcB points to the second input vector of length srcBLen. * The output result is written to pDst and the calling function must allocate srcALen+srcBLen-1 words for the result. * * \par * Conceptually, when two signals a[n] and b[n] are convolved, * the signal b[n] slides over a[n]. * 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: * * 
    
 *                   a[n] * b[n] = b[n] * a[n].    
 *
* * \par * This means that switching the A and B arguments to the convolution functions has no effect. * * Fixed-Point Behavior * * \par * 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. * * * Fast Versions * * \par * 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. * * * Opt Versions * * \par * 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( float32_t * pSrcA, uint32_t srcALen, float32_t * pSrcB, uint32_t srcBLen, float32_t * pDst) { #ifndef ARM_MATH_CM0_FAMILY /* Run the below code for Cortex-M4 and Cortex-M3 */ float32_t *pIn1; /* inputA pointer */ float32_t *pIn2; /* inputB pointer */ float32_t *pOut = pDst; /* output pointer */ float32_t *px; /* Intermediate inputA pointer */ float32_t *py; /* Intermediate inputB pointer */ float32_t *pSrc1, *pSrc2; /* Intermediate pointers */ float32_t sum, acc0, acc1, acc2, acc3; /* Accumulator */ float32_t x0, x1, x2, x3, c0; /* Temporary variables to hold state and coefficient values */ uint32_t j, k, count, blkCnt, blockSize1, blockSize2, blockSize3; /* loop counters */ /* 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 * ----------------------*/ /* The first stage starts here */ while(blockSize1 > 0u) { /* Accumulator is made zero for every iteration */ sum = 0.0f; /* Apply loop unrolling and compute 4 MACs simultaneously. */ k = count >> 2u; /* 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. */ 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 the loop counter */ k--; } /* If the count is not a multiple of 4, compute any remaining MACs here. ** No loop unrolling is used. */ k = count % 0x4u; 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; /* Update the inputA and inputB pointers for next MAC calculation */ py = pIn2 + count; px = pIn1; /* Increment the MAC count */ count++; /* Decrement the 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) { /* Loop unroll over blockSize2, by 4 */ blkCnt = blockSize2 >> 2u; while(blkCnt > 0u) { /* Set all accumulators to zero */ acc0 = 0.0f; acc1 = 0.0f; acc2 = 0.0f; acc3 = 0.0f; /* read x[0], x[1], x[2] samples */ x0 = *(px++); x1 = *(px++); x2 = *(px++); /* Apply loop unrolling and compute 4 MACs simultaneously. */ k = srcBLen >> 2u; /* 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-accumulates */ /* 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-accumulates */ /* 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-accumulates */ /* 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--; } /* 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; while(blkCnt > 0u) { /* Accumulator is made zero for every iteration */ sum = 0.0f; /* Apply loop unrolling and compute 4 MACs simultaneously. */ k = srcBLen >> 2u; /* 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. */ while(k > 0u) { /* Perform the multiply-accumulates */ sum += *px++ * *py--; sum += *px++ * *py--; sum += *px++ * *py--; sum += *px++ * *py--; /* Decrement the loop counter */ 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) { /* 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; /* Apply loop unrolling and compute 4 MACs simultaneously. */ k = blockSize3 >> 2u; /* 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. */ while(k > 0u) { /* 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 the loop counter */ k--; } /* If the blockSize3 is not a multiple of 4, compute any remaining MACs here. ** No loop unrolling is used. */ k = blockSize3 % 0x4u; while(k > 0u) { /* Perform the multiply-accumulates */ /* sum += x[srcALen-1] * y[srcBLen-1] */ sum += *px++ * *py--; /* Decrement the 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 /* Run the below code for Cortex-M0 */ float32_t *pIn1 = pSrcA; /* inputA pointer */ 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 /* #ifndef ARM_MATH_CM0_FAMILY */ } /** * @} end of Conv group */