Challenges of Computation-in-Memory Circuits for AI Edge Applications

In the conventionally von-Neumann computing architecture, the separation of memory and computing units (i.e., the memory-wall bottleneck) poses notable challenges to the development of energy-efficient AI edge devices. Computing-in-memory (CIM) is an emerging approach to breaking through this bottleneck. This paper outlines the background of computing-in-memory macros as well as on-going challenges and trends in the development of this technology.