PIPECIM: Energy-Efficient Pipelined Computing-in-Memory Computation Engine With Sparsity-Aware Technique

Abstract

Computing-in-memory (CIM) architecture has become a promising solution to improve the parallelism of the multiply-and-accumulation (MAC) operation for artificial intelligence (AI) processors. Recently, revived CIM engine partly relieves the memory wall issue by integrating computation in/with the memory. However, current CIM solutions still require large data movements with the increase of the practical neural network model and massive input data. Previous CIM works only considered computation without concern for the memory attribute, leading to a low memory computing ratio. This article presents a static-random access-memory (SRAM)-based digital CIM macro supporting pipeline mode and computation-memory-aware technique to improve the memory computing ratio. We develop a novel weight driver with fine-grained ping-pong operation, avoiding the computation stall caused by weight update. Based on our evaluation, the peak energy efficiency is 19.78 TOPS/W at the 22-nm technology node, 8-bit width, and 50% sparsity of the input feature map.