ReFFT: An Energy-Efficient RRAM-Based FFT Accelerator

Abstract

The fast Fourier transform (FFT) is a highly efficient algorithm for computing the discrete Fourier transform (DFT). It is widely employed in various applications, including digital communication, image processing, and signal analysis. Recently, in-memory computing architectures based on emerging technologies, such as resistive RAM (RRAM), have demonstrated promising performance with low hardware cost for data-intensive applications. However, directly mapping FFT onto RRAM crossbars is challenging because the algorithm relies on many small, sequential butterfly operations, while crossbars are optimized for large-scale, highly parallel vector–matrix multiplications (VMMs). In this article, we introduce ReFFT, a system architecture that reformulates FFT computations for efficient execution on RRAM crossbars. ReFFT combines the reduced computational complexity of FFT with the parallel VMM capability of RRAM. We incorporate measured device data into our framework to analyze the effect of variability and develop an adaptive mapping scheme that improves twiddle-factor programming accuracy, leading to a 9.9 dB peak signal-to-noise ratio (PSNR) improvement for a 256-point FFT. Compared with prior RRAM-based DFT designs, ReFFT achieves up to $\mathbf {4.6\times }$ and $\mathbf {19.5 \times }$ higher energy efficiency for 256- and 2048-point FFTs, respectively. The system is further validated in digital communication and satellite image compression tasks.