Emerging Memory-Based Chip Development for Neuromorphic Computing: Status, Challenges, and Perspectives

In this article, we review the development of emerging memory-based neuromorphic computing. First, we discuss the motivation and advantages of this approach. We then summarize the mechanisms and electrical behaviors of commonly used emerging memory devices as well as their application characteristics in various neuromorphic computing paradigms, including computing in memory (CIM) and other biologically plausible brain-like computing. Next, we introduce the principles of CIM and analyze its characteristics from the perspective of parallelism, precision, and signal domains. We also summarize some representative works on chip and system implementations. Additionally, we review the state of the art of other brain-like computing approaches, such as spiking neural networks (SNNs) and reservoir computing (RC). Finally, we provide insights and conclusions on the potential and obstacles of developing neuromorphic computing based on emerging memory, from both the device and system levels.