Challenges and Trends of Nonvolatile In-Memory-Computation Circuits for AI Edge Devices

Abstract

Nonvolatile memory (NVM)-based computing-in-memory (nvCIM) is a promising candidate for artificial intelligence (AI) edge devices to overcome the latency and energy consumption imposed by the movement of data between memory and processors under the von Neumann architecture. This paper explores the background and basic approaches to nvCIM implementation, including input methodologies, weight formation and placement, and readout and quantization methods. This paper outlines the major challenges in the further development of nvCIM macros and reviews trends in recent silicon-verified devices.