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

RRAM crossbars provide a promising hardware platform to accelerate matrix-vector multiplication in deep neural networks (DNNs). To exploit the efficiency of RRAM crossbars, extensive research examining architecture, data representation, logic design 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 examples on these perspectives on RRAM crossbars, including data representation with pulse widths, architecture improvement, implementation of logic functions using RRAM cells, and efficient programming of RRAM devices for accelerating DNNs.