Unconventional computing based on magnetic tunnel junction

doi:10.1007/s00339-022-06365-4

	Unconventional computing based on magnetic tunnel junction
	Cai, Baofang 1; He, Yihan 1; Xin, Yue2 ; Yuan, Zhengping2 ; Zhang, Xue2 ; Zhu, Zhifeng2,3 ; Liang, Gengchiau 1
	2023-04-01
发表期刊	APPLIED PHYSICS A: MATERIALS SCIENCE AND PROCESSING; (IF:2.5[JCR-2023],2.6[5-Year])
ISSN	0947-8396
EISSN	1432-0630
卷号	129 期号:4
发表状态	已发表
DOI	10.1007/s00339-022-06365-4
摘要	The conventional computing method based on the von Neumann architecture is limited by a series of problems such as high energy consumption, finite data exchange bandwidth between processors and storage media, etc., and it is difficult to achieve higher computing efficiency. A more efficient unconventional computing architecture is urgently needed to overcome these problems. Neuromorphic computing and stochastic computing have been considered to be two competitive candidates for unconventional computing, due to their extraordinary potential for energy-efficient and high-performance computing. Although conventional electronic devices can mimic the topology of the human brain, these require high power consumption and large area. Spintronic devices represented by magnetic tunnel junctions (MTJs) exhibit remarkable high-energy efficiency, non-volatility, and similarity to biological nervous systems, making them one of the promising candidates for unconventional computing. In this work, we review the fundamentals of MTJs as well as the development of MTJ-based neurons, synapses, and probabilistic-bit. In the section on neuromorphic computing, we review a variety of neural networks composed of MTJ-based neurons and synapses, including multilayer perceptrons, convolutional neural networks, recurrent neural networks, and spiking neural networks, which are the closest to the biological neural system. In the section on stochastic computing, we review the applications of MTJ-based p-bits, including Boltzmann machines, Ising machines, and Bayesian networks. Furthermore, the challenges to developing these novel technologies are briefly discussed at the end of each section.
关键词	Magnetic tunnel junction Neuromorphic computing Spintronic neuron Spintronic synapse Stochastic switching Unconventional computing
学科门类	Chemistry (all) ; Materials Science (all)
URL	查看原文
收录类别	SCOPUS ; EI ; SCI
语种	英语
资助项目	National Key R&D Program of China[2022YFB4401700] ; Shanghai Sailing Program[20YF1430400] ; NSFC["MOE-2019-T2-2-215","FRC-A-8000194-01-00","12104301"] ; null[MOE-2017- T2-2-114]
WOS研究方向	Materials Science ; Physics
WOS类目	Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号	WOS:000943247300002
出版者	SPRINGER HEIDELBERG
EI入藏号	20231113739986
EI主题词	Energy efficiency
EI分类号	461.9 Biology ; 525.2 Energy Conservation ; 525.3 Energy Utilization ; 722.1 Data Storage, Equipment and Techniques ; 723.2 Data Processing and Image Processing
原始文献类型	Article
Scopus 记录号	2-s2.0-85149928709
来源库	SCOPUS
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/286531
专题	信息科学与技术学院信息科学与技术学院_硕士生信息科学与技术学院_博士生信息科学与技术学院_PI研究组_祝智峰组
通讯作者	Zhu, Zhifeng; Liang, Gengchiau
作者单位	1.Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore 2.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China 3.Shanghai Engn Res Ctr Energy Efficient & Custom AI, Shanghai 201210, Peoples R China
通讯作者单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Cai, Baofang,He, Yihan,Xin, Yue,et al. Unconventional computing based on magnetic tunnel junction[J]. APPLIED PHYSICS A: MATERIALS SCIENCE AND PROCESSING;,2023,129(4).
APA	Cai, Baofang.,He, Yihan.,Xin, Yue.,Yuan, Zhengping.,Zhang, Xue.,...&Liang, Gengchiau.(2023).Unconventional computing based on magnetic tunnel junction.APPLIED PHYSICS A: MATERIALS SCIENCE AND PROCESSING;,129(4).
MLA	Cai, Baofang,et al."Unconventional computing based on magnetic tunnel junction".APPLIED PHYSICS A: MATERIALS SCIENCE AND PROCESSING; 129.4(2023).