Implementation of Discrete Fourier Transform using RRAM Arrays with Quasi-Analog Mapping for High-Fidelity Medical Image Reconstruction
Han Zhao, Zhengwu Liu, Jianshi Tang, Bin Gao, Ying Zhou, Peng Yao, Yue Xi, He Qian, Huaqiang Wu
Abstract
In this paper, we report the first experimental implementation of discrete Fourier transform (DFT) on analog resistive switching random-access memory (RRAM) arrays with computing-in-memory (CIM). Considering the features of transform matrix, we developed a novel conductance mapping strategy, namely quasi-analog mapping (QAM), to realize high-precision mapping by taking advantage of the analog switching characteristics of our RRAM. Based on the RRAM-based DFT models, high-fidelity medical image reconstruction was further demonstrated, achieving a software-comparable peak signal-to-noise ratio (PSNR) of 26.1 dB. Compared to the commonly used quantized mapping (QM), QAM enhanced the image reconstruction quality and showed strong robustness to RRAM read noise. RRAM-implemented DFT also achieved ∼128× higher energy efficiency than CPU. This work provides a general strategy for using RRAM array with CIM feature to accelerate signal processing algorithms.