Please use this identifier to cite or link to this item: https://doi.org/10.3389/fnano.2021.645995
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dc.titleAdvances in Memristor-Based Neural Networks
dc.contributor.authorXu, W.
dc.contributor.authorWang, J.
dc.contributor.authorYan Xiaobing
dc.date.accessioned2022-10-13T01:12:53Z
dc.date.available2022-10-13T01:12:53Z
dc.date.issued2021-03-24
dc.identifier.citationXu, W., Wang, J., Yan Xiaobing (2021-03-24). Advances in Memristor-Based Neural Networks. Frontiers in Nanotechnology 3 : 645995. ScholarBank@NUS Repository. https://doi.org/10.3389/fnano.2021.645995
dc.identifier.issn2673-3013
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/232817
dc.description.abstractThe rapid development of artificial intelligence (AI), big data analytics, cloud computing, and Internet of Things applications expect the emerging memristor devices and their hardware systems to solve massive data calculation with low power consumption and small chip area. This paper provides an overview of memristor device characteristics, models, synapse circuits, and neural network applications, especially for artificial neural networks and spiking neural networks. It also provides research summaries, comparisons, limitations, challenges, and future work opportunities. Copyright © 2021 Xu, Wang and Yan.
dc.publisherFrontiers Media S.A.
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScopus OA2021
dc.subjectartificial intelligence
dc.subjectartificial neural network
dc.subjectintegrated circuit
dc.subjectmemristor
dc.subjectspiking neural network
dc.typeReview
dc.contributor.departmentMATERIALS SCIENCE AND ENGINEERING
dc.description.doi10.3389/fnano.2021.645995
dc.description.sourcetitleFrontiers in Nanotechnology
dc.description.volume3
dc.description.page645995
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