From macro to microarchitecture:reviews and trends of SRAM-based compute-in-memory circuits  

作　　者：Zhaoyang ZHANG Jinwu CHEN Xi CHEN An GUO Bo WANG Tianzhu XIONG Yuyao KONG Xingyu PU Shengnan HE Xin SI Jun YANG 

机构地区：[1]National Application Specific Integrated Circuit Center,Southeast University,Nanjing,210096,China

出　　处：《Science China(Information Sciences)》2023年第10期38-56,共19页中国科学（信息科学）（英文版）

基　　金：supported by National Key R&D Program of China(Grant No.2022ZD0118902);National Natural Science Foundation of China(Grant Nos.92264203,62204036)。

摘　　要：The rapid growth of CMOS logic circuits has surpassed the advancements in memory access,leading to significant“memory wall”bottlenecks,particularly in artificial intelligence applications.To address this challenge,compute-in-memory(CIM)has emerged as a promising approach to enhance the performance,area efficiency,and energy efficiency of computing systems.By enabling memory cells to perform parallel computations,CIM improves data reuse and minimizes data movement between the memory and the processor.This study conducts a comprehensive review of various domains of SRAM-based CIM macros and their associated computing paradigms.Additionally,it presents a survey of recent SRAM-CIM macros,with a specific focus on the key challenges and design tradeoffs involved.Furthermore,this research identifies potential future trends in SRAM-CIM macro-level design,including hybrid computing,precision enhancement,and operator reconfiguration.These trends aim to resolve the tradeoff between computational accuracy,energy efficiency,and support for diverse operators within the SRAM-CIM framework.At the microarchitecture level,two possible solutions for tradeoffs are proposed:chiplet integration and sparsity optimization.Finally,research perspectives are proposed for future development.

关 键 词：artificial intelligence(AI) compute-in-memory(CIM) static random access memory(SRAM) 

分 类 号：TP333[自动化与计算机技术—计算机系统结构] TP18[自动化与计算机技术—计算机科学与技术]