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Article: Hybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks

TitleHybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks
Authors
KeywordsMemristor
Hybrid neuron
In-situ learning
Fully hardware
Spiking neural network
Issue Date2021
PublisherElsevier B.V. and Science China Press. The Journal's web site is located at http://www.sciencedirect.com/science/journal/20959273?sdc=1
Citation
Science Bulletin, 2021, v. 66 n. 16, p. 1624-1633 How to Cite?
AbstractSpiking neural network, inspired by the human brain, consisting of spiking neurons and plastic synapses, is a promising solution for highly efficient data processing in neuromorphic computing. Recently, memristor-based neurons and synapses are becoming intriguing candidates to build spiking neural networks in hardware, owing to the close resemblance between their device dynamics and the biological counterparts. However, the functionalities of memristor-based neurons are currently very limited, and a hardware demonstration of fully memristor-based spiking neural networks supporting in-situ learning is very challenging. Here, a hybrid spiking neuron combining a memristor with simple digital circuits is designed and implemented in hardware to enhance neuron functions. The hybrid neuron with memristive dynamics not only realizes the basic leaky integrate-and-fire neuron function but also enables the in-situ tuning of the connected synaptic weights. Finally, a fully hardware spiking neural network with the hybrid neurons and memristive synapses is experimentally demonstrated for the first time, and in-situ Hebbian learning is achieved with this network. This work opens up a way towards the implementation of spiking neurons, supporting in-situ learning for future neuromorphic computing systems.
Persistent Identifierhttp://hdl.handle.net/10722/306149
ISSN
2023 Impact Factor: 18.8
2023 SCImago Journal Rankings: 2.807
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, X-
dc.contributor.authorLu, J-
dc.contributor.authorWang, Z-
dc.contributor.authorWang, R-
dc.contributor.authorWei, J-
dc.contributor.authorShi, T-
dc.contributor.authorDou, C-
dc.contributor.authorWu, Z-
dc.contributor.authorZhu, J-
dc.contributor.authorShang, D-
dc.contributor.authorXing, G-
dc.contributor.authorChan, M-
dc.contributor.authorLiu, Q-
dc.contributor.authorLiu, M-
dc.date.accessioned2021-10-20T10:19:29Z-
dc.date.available2021-10-20T10:19:29Z-
dc.date.issued2021-
dc.identifier.citationScience Bulletin, 2021, v. 66 n. 16, p. 1624-1633-
dc.identifier.issn2095-9273-
dc.identifier.urihttp://hdl.handle.net/10722/306149-
dc.description.abstractSpiking neural network, inspired by the human brain, consisting of spiking neurons and plastic synapses, is a promising solution for highly efficient data processing in neuromorphic computing. Recently, memristor-based neurons and synapses are becoming intriguing candidates to build spiking neural networks in hardware, owing to the close resemblance between their device dynamics and the biological counterparts. However, the functionalities of memristor-based neurons are currently very limited, and a hardware demonstration of fully memristor-based spiking neural networks supporting in-situ learning is very challenging. Here, a hybrid spiking neuron combining a memristor with simple digital circuits is designed and implemented in hardware to enhance neuron functions. The hybrid neuron with memristive dynamics not only realizes the basic leaky integrate-and-fire neuron function but also enables the in-situ tuning of the connected synaptic weights. Finally, a fully hardware spiking neural network with the hybrid neurons and memristive synapses is experimentally demonstrated for the first time, and in-situ Hebbian learning is achieved with this network. This work opens up a way towards the implementation of spiking neurons, supporting in-situ learning for future neuromorphic computing systems.-
dc.languageeng-
dc.publisherElsevier B.V. and Science China Press. The Journal's web site is located at http://www.sciencedirect.com/science/journal/20959273?sdc=1-
dc.relation.ispartofScience Bulletin-
dc.subjectMemristor-
dc.subjectHybrid neuron-
dc.subjectIn-situ learning-
dc.subjectFully hardware-
dc.subjectSpiking neural network-
dc.titleHybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks-
dc.typeArticle-
dc.identifier.emailWang, Z: zrwang@eee.hku.hk-
dc.identifier.authorityWang, Z=rp02714-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.scib.2021.04.014-
dc.identifier.scopuseid_2-s2.0-85111397913-
dc.identifier.hkuros327763-
dc.identifier.volume66-
dc.identifier.issue16-
dc.identifier.spage1624-
dc.identifier.epage1633-
dc.identifier.isiWOS:000686908500009-
dc.publisher.placeUnited States-

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