File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Memristive Crossbar Arrays for Storage and Computing Applications

TitleMemristive Crossbar Arrays for Storage and Computing Applications
Authors
KeywordsArtificial neural networks
Crossbar arrays
Memory storage
Neuromorphic computing
Issue Date2021
PublisherWiley Open Access. The Journal's web site is located at https://onlinelibrary.wiley.com/journal/26404567
Citation
Advanced Intelligent Systems, 2021, v. 3 n. 9, article no. 2100017 How to Cite?
AbstractThe emergence of memristors with potential applications in data storage and artificial intelligence has attracted wide attentions. Memristors are assembled in crossbar arrays with data bits encoded by the resistance of individual cells. Despite the proposed high density and excellent scalability, the sneak-path current causing cross interference impedes their practical applications. Therefore, developing novel architectures to mitigate sneak-path current and improve efficiency, reliability, and stability may benefit next-generation storage-class memory (SCM). Moreover, conventional digital computers face the von-Neumann bottleneck and the slowdown of transistors’ scaling, imposing a big challenge to hardware artificial intelligence. Memristive crossbar features colocation of memory and processing units, as well as superior scalability, making it a promising candidate for hardware accelerating machine learning and neuromorphic computing. Herein, first, crossbar architecture is introduced. Then, for storage, the origin of sneak-path current is reviewed and techniques to mitigate this issue from the angle of materials and circuits are discussed. Computing wise, the applications of memristive crossbars in both machine learning and neuromorphic computing are surveyed, focusing on the structure of unit cells, the network topology, and the learning types. Finally, a perspective on future engineering and applications of memristive crossbars is discussed.
Persistent Identifierhttp://hdl.handle.net/10722/305339
ISSN
2023 Impact Factor: 6.8
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, H-
dc.contributor.authorWang, S-
dc.contributor.authorZhang, X-
dc.contributor.authorWang, W-
dc.contributor.authorYang, R-
dc.contributor.authorSun, Z-
dc.contributor.authorFeng, W-
dc.contributor.authorLin, P-
dc.contributor.authorWang, Z-
dc.contributor.authorSun, L-
dc.contributor.authorYao, Y-
dc.date.accessioned2021-10-20T10:08:01Z-
dc.date.available2021-10-20T10:08:01Z-
dc.date.issued2021-
dc.identifier.citationAdvanced Intelligent Systems, 2021, v. 3 n. 9, article no. 2100017-
dc.identifier.issn2640-4567-
dc.identifier.urihttp://hdl.handle.net/10722/305339-
dc.description.abstractThe emergence of memristors with potential applications in data storage and artificial intelligence has attracted wide attentions. Memristors are assembled in crossbar arrays with data bits encoded by the resistance of individual cells. Despite the proposed high density and excellent scalability, the sneak-path current causing cross interference impedes their practical applications. Therefore, developing novel architectures to mitigate sneak-path current and improve efficiency, reliability, and stability may benefit next-generation storage-class memory (SCM). Moreover, conventional digital computers face the von-Neumann bottleneck and the slowdown of transistors’ scaling, imposing a big challenge to hardware artificial intelligence. Memristive crossbar features colocation of memory and processing units, as well as superior scalability, making it a promising candidate for hardware accelerating machine learning and neuromorphic computing. Herein, first, crossbar architecture is introduced. Then, for storage, the origin of sneak-path current is reviewed and techniques to mitigate this issue from the angle of materials and circuits are discussed. Computing wise, the applications of memristive crossbars in both machine learning and neuromorphic computing are surveyed, focusing on the structure of unit cells, the network topology, and the learning types. Finally, a perspective on future engineering and applications of memristive crossbars is discussed.-
dc.languageeng-
dc.publisherWiley Open Access. The Journal's web site is located at https://onlinelibrary.wiley.com/journal/26404567-
dc.relation.ispartofAdvanced Intelligent Systems-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectArtificial neural networks-
dc.subjectCrossbar arrays-
dc.subjectMemory storage-
dc.subjectNeuromorphic computing-
dc.titleMemristive Crossbar Arrays for Storage and Computing Applications-
dc.typeArticle-
dc.identifier.emailWang, Z: zrwang@eee.hku.hk-
dc.identifier.authorityWang, Z=rp02714-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1002/aisy.202100017-
dc.identifier.hkuros327770-
dc.identifier.volume3-
dc.identifier.issue9-
dc.identifier.spagearticle no. 2100017-
dc.identifier.epagearticle no. 2100017-
dc.identifier.isiWOS:000669972200001-
dc.publisher.placeGermany-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats