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- Publisher Website: 10.1007/s11432-018-9425-1
- Scopus: eid_2-s2.0-85047496703
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Article: Artificial neural networks based on memristive devices
Title | Artificial neural networks based on memristive devices |
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Authors | |
Keywords | neural networks neuromorphic computing memristors |
Issue Date | 2018 |
Citation | Science China Information Sciences, 2018, v. 61, n. 6, article no. 060423 How to Cite? |
Abstract | © 2018, Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature. The advent of memristive devices and the continuing research and development in the field of neuromorphic computing show great potential as an alternative to traditional von Neumann computing and bring us ever closer to realizing a true “thinking machine”. Novel neural network architectures and algorithms inspired by the brain are becoming more and more attractive to keep up with computing needs, relying on intrinsic parallelism and reduced power consumption to outperform more conventional computing methods. This article provides an overview of various neural networks with an emphasis on networks based on memristive emerging devices, with the advantages of memristor neural networks compared with pure complementary metal oxide semiconductor (CMOS) implementations. A general description of neural networks is presented, followed by a survey of prominent CMOS networks, and finally networks implemented using emerging memristive devices are discussed, along with the motivation for developing memristor based networks and the computational potential these networks possess. |
Persistent Identifier | http://hdl.handle.net/10722/286966 |
ISSN | 2023 Impact Factor: 7.3 2023 SCImago Journal Rankings: 1.882 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ravichandran, Vignesh | - |
dc.contributor.author | Li, Can | - |
dc.contributor.author | Banagozar, Ali | - |
dc.contributor.author | Yang, J. Joshua | - |
dc.contributor.author | Xia, Qiangfei | - |
dc.date.accessioned | 2020-09-07T11:46:08Z | - |
dc.date.available | 2020-09-07T11:46:08Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Science China Information Sciences, 2018, v. 61, n. 6, article no. 060423 | - |
dc.identifier.issn | 1674-733X | - |
dc.identifier.uri | http://hdl.handle.net/10722/286966 | - |
dc.description.abstract | © 2018, Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature. The advent of memristive devices and the continuing research and development in the field of neuromorphic computing show great potential as an alternative to traditional von Neumann computing and bring us ever closer to realizing a true “thinking machine”. Novel neural network architectures and algorithms inspired by the brain are becoming more and more attractive to keep up with computing needs, relying on intrinsic parallelism and reduced power consumption to outperform more conventional computing methods. This article provides an overview of various neural networks with an emphasis on networks based on memristive emerging devices, with the advantages of memristor neural networks compared with pure complementary metal oxide semiconductor (CMOS) implementations. A general description of neural networks is presented, followed by a survey of prominent CMOS networks, and finally networks implemented using emerging memristive devices are discussed, along with the motivation for developing memristor based networks and the computational potential these networks possess. | - |
dc.language | eng | - |
dc.relation.ispartof | Science China Information Sciences | - |
dc.subject | neural networks | - |
dc.subject | neuromorphic computing | - |
dc.subject | memristors | - |
dc.title | Artificial neural networks based on memristive devices | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/s11432-018-9425-1 | - |
dc.identifier.scopus | eid_2-s2.0-85047496703 | - |
dc.identifier.volume | 61 | - |
dc.identifier.issue | 6 | - |
dc.identifier.spage | article no. 060423 | - |
dc.identifier.epage | article no. 060423 | - |
dc.identifier.eissn | 1869-1919 | - |
dc.identifier.isi | WOS:000434369500020 | - |
dc.identifier.issnl | 1869-1919 | - |