Monolithically Integrated RRAM- and CMOS-Based In-Memory Computing Optimizations for Efficient Deep Learning

Resistive RAM (RRAM) has been presented as a promising memory technology toward deep neural network (DNN) hardware design, with nonvolatility, high density, high ON/OFF ratio, and compatibility with logic process. However, prior RRAM works for DNNs have shown limitations on parallelism for in-memory computing, array efficiency with large peripheral circuits, multilevel analog operation, and demonstration of monolithic integration. In this...