Unconventional computing based on magnetic tunnel junction
2023-04-01
Source PublicationAPPLIED PHYSICS A: MATERIALS SCIENCE AND PROCESSING;
ISSN0947-8396
EISSN1432-0630
Volume129Issue:4
Status已发表
DOI10.1007/s00339-022-06365-4
AbstractThe 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.
KeywordMagnetic tunnel junction Neuromorphic computing Spintronic neuron Spintronic synapse Stochastic switching Unconventional computing
MOST Discipline CatalogueChemistry (all) ; Materials Science (all)
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Indexed BySCOPUS ; EI ; SCI
Language英语
Funding ProjectNational 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 Research AreaMaterials Science ; Physics
WOS SubjectMaterials Science, Multidisciplinary ; Physics, Applied
WOS IDWOS:000943247300002
PublisherSPRINGER HEIDELBERG
EI Accession Number20231113739986
EI KeywordsEnergy efficiency
EI Classification Number461.9 Biology ; 525.2 Energy Conservation ; 525.3 Energy Utilization ; 722.1 Data Storage, Equipment and Techniques ; 723.2 Data Processing and Image Processing
Original Document TypeArticle
Scopus ID2-s2.0-85149928709
Source DataSCOPUS
Citation statistics
Document Type期刊论文
Identifierhttps://kms.shanghaitech.edu.cn/handle/2MSLDSTB/286531
Collection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
Corresponding AuthorZhu, Zhifeng; Liang, Gengchiau
Affiliation
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
Corresponding Author AffilicationSchool of Information Science and Technology
Recommended Citation
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).
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