A survey of in-spin transfer torque MRAM computing  

作　　者：Hao CAI Bo LIU Juntong CHEN Lirida NAVINER Yongliang ZHOU Zhen WANG Jun YANG 

机构地区：[1]National ASIC System Engineering Research Center,Southeast University,Nanjing 210096,China [2]Laboratoire TYaitement et Communication de l'Information,Telecom Paris,Pa/aiseaw 91120,Prance [3]Nanjing Prochip Electronic Technology Co.,Ltd.,Nanjing 210000,China

出　　处：《Science China(Information Sciences)》2021年第6期26-40,共15页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant No.61904028)。

摘　　要：In traditional von Neumann computing architectures,the essential transfer of data between the processor and memory hierarchies limits the computational efficiency of next-generation system-on-a-chip.The emerging in-memory computing(IMC)approach addresses this issue and facilitates the movement of significant data and rapid computations.Among the different memory types,intrinsic energy efficiency is demonstrated by in-magnetic random access memory(MRAM)computing with a low-power spintronic magnetic tunnel junction device and hybrid integration at an advanced complementary metal-oxide semiconductor node.This study reviews state-of-the-art techniques for managing IMC with an emphasis on spin-transfer torque-MRAM computing via design schemes at the bit-cell,circuit,and system levels.In addition,this study presents effective design techniques and potential challenges and demonstrates the existing limitations of in-MRAM computing and potential methods for overcoming these issues.This study also considers the design technology co-optimization from the IMC perspective.

关 键 词：spin-transfer torque-magnetoresistive random access memory in-memory computing magnetic tunnel junction analog computing nonvolatile memory Boolean logic neural network 

分 类 号：TP333[自动化与计算机技术—计算机系统结构]