Area-Efficient Error-Resilient Discrete Fourier Transformation Design using Stochastic Computing
Bo Yuan, Yanzhi Wang, Zhongfeng Wang · 2016
Discrete Fourier Transformation (DFT)/Fast Fourier Transformation (FFT) are the widely used techniques in numerous modern signal processing applications. In general, because of their inherent multiplication-intensive characteristics, the hardware implementations of DFT/FFT usually require a large amount of hardware resource, which limits their applications in area-constraint scenarios. To overcome this challenge, this paper, for the first time, proposes area-efficient error-resilient DFT designs using stochastic computing. By leveraging low-complexity stochastic multipliers, two types of stochastic DFT design are presented with significant reduction in overall area. Analysis results show that compared with the conventional design, the proposed two 256-point stochastic DFT designs achieve 76% and 62% reduction in area, respectively. More importantly, these stochastic DFT designs also show much stronger error-resilience, which is very attractive in nanoscale CMOS era.