Reliable Computing of ReRAM Based Compute-in-Memory Circuits for AI Edge Devices

Abstract

Compute-in-memory macros based on non-volatile memory (nvCIM) are a promising approach to break through the memory bottleneck for artificial intelligence (AI) edge devices; however, the development of these devices involves unavoidable tradeoffs between reliability, energy efficiency, computing latency, and readout accuracy. This paper outlines the background of ReRAM-based nvCIM as well as the major challenges in its further development, including process variation in ReRAM devices and transistors and the small signal margins associated with variation in input-weight patterns. This paper also investigates the error model of a nvCIM macro, and the correspondent degradation of inference accuracy as a function of error model when using nvCIM macros. Finally, we summarize recent trends and advances in the development of reliable ReRAM-based nvCIM macro.