A Reinforcement Learning Methodology for The Search of SRAM CIM-based Accelerator Configuration

Abstract

Computing-in-memories (CIM) is recognized as a useful design technique for eliminating the Von Neumann bottleneck. However, there is a need for circuit designers to determine the configuration (i.e., design parameters) of CIM-based accelerators. Note that the configuration has influences on circuit area, throughput, and energy efficiency. In this paper, we focus on the SRAM CIM-based accelerator design. A reinforcement learning methodology is proposed to assist circuit designers to find the most suitable configuration. Experiment data show that the proposed methodology works well in practice.