Progress and challenges of phase change memory for in-memory computing

Abstract

Analog in-memory computing (AIMC) using nonvolatile memories (NVMs) is very promising for achieving low latency and high energy efficiency for deep neural network (DNN) acceleration. There has been significant progress in using phase change memory (PCM) for analog IMC in recent years, especially for DNN inference applications, for both electrical and optical computing. In this paper, we present a review of these works, focusing primarily on PCMs for electrical computing, and including an overview on PCMs for optical computing. For electrical computing using PCM, we review the progress in both the device and the system level. On the device level, we first discuss the impact of PCM characteristics on AIMC computing and introduce relevant benchmarking methods. We then discuss progress in improving PCM devices for AIMC mainly by reducing nonidealities including resistance drift, read noise, and yield. We also discuss progress in programming characteristics that limit the density and programming power. On the system level, we discuss the optimization of memory cells, weight mapping methods, advanced drift compensation algorithms, and co-design considerations. We then review progress in AIMC energy efficiency studies and recent chip demonstrations. Since there is a growing interest in using PCM for photonic computing recently, we give an overview of this area including the device structures and system demonstrations. In the end, we briefly summarize the status and outlook of this field.