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ERROR ANALYSIS AND COMPLEXITY OPTIMIZATION FOR THE MULTIPLIER-LESS FFT-LIKE transformation (ML-FFT)

机译:乘法器较少的FFT变换(ML-FFT)的误差分析和复杂性优化

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This paper studies the effect of the signal round-off errors on the accuracies of the multiplier-less Fast Fourier Transform-like transformation (ML-FFT). The idea of the ML-FFT is to parameterize the twiddle factors in the conventional FFT algorithm as certain rotation-like matrices and approximate the associated parameters inside these matrices by the sum-of-power-of-two (SOPOT) or canonical signed digits (CSD) representations. The error due to the SOPOT approximation is called the coefficient round-off error. Apart from this error, signal round-off error also occurs because of insufficient wordlengths. Using a recursive noise model of these errors, the minimum hardware to realize the ML-FFT subject to the prescribed output bit accuracy can be obtained using a random search algorithm. A design example is given to demonstrate the effectiveness of the proposed approach.
机译:本文研究了信号循环误差对乘法器快速傅里叶变换变换(ML-FFT)的精度的影响。 ML-FFT的思想是将传统FFT算法中的旋转因子作为某些旋转等矩阵进行参数化,并通过两个功率的幂(SOPOT)或规范签名的数字近似于这些矩阵内的相关参数(CSD)表示。由于Sopot近似引起的错误称为系数循环误差。除了此错误之外,还因为WordLengths不足而发生了信号舍入错误。使用这些误差的递归噪声模型,可以使用随机搜索算法获得要实现经过规定的输出比特精度的ML-FFT的最小硬件。给出了设计示例来证明所提出的方法的有效性。

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