RRAM-based Neuromorphic Computing: Data Representation, Architecture, Logic, and Programming

Abstract

RRAM crossbars provide a promising hardware plat-form to accelerate matrix-vector multiplication in deep neural networks (DNNs). To exploit the efficiency of RRAM crossbars, extensive research ex-amining architecture, data representation, logic de-sign as well as device programming should be conducted. This extensive scope of research aspects is enabled and required by the versatility of RRAM cells and their organization in a computing system. These research aspects affect or benefit each other. Therefore, they should be considered systematically to achieve an efficient design in terms of design complexity and computational performance in accelerating DNNs. In this paper, we illustrate study exam-ples on these perspectives on RRAM crossbars, in-cluding data representation with pulse widths, archi-tecture improvement, implementation of logic functions using RRAM cells, and efficient programming of RRAM devices for accelerating DNNs.