DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhang, Wenqiang | ko |
dc.contributor.author | Gao, Bin | ko |
dc.contributor.author | Tang, Jianshi | ko |
dc.contributor.author | Yao, Peng | ko |
dc.contributor.author | Yu, Shimeng | ko |
dc.contributor.author | Chang, Meng-Fan | ko |
dc.contributor.author | Yoo, Hoi-Jun | ko |
dc.contributor.author | Qian, He | ko |
dc.contributor.author | Wu, Huaqiang | ko |
dc.date.accessioned | 2020-08-18T05:55:11Z | - |
dc.date.available | 2020-08-18T05:55:11Z | - |
dc.date.created | 2020-08-10 | - |
dc.date.created | 2020-08-10 | - |
dc.date.issued | 2020-07 | - |
dc.identifier.citation | NATURE ELECTRONICS, v.3, no.7, pp.371 - 382 | - |
dc.identifier.issn | 2520-1131 | - |
dc.identifier.uri | http://hdl.handle.net/10203/275856 | - |
dc.description.abstract | The rapid development of artificial intelligence (AI) demands the rapid development of domain-specific hardware specifically designed for AI applications. Neuro-inspired computing chips integrate a range of features inspired by neurobiological systems and could provide an energy-efficient approach to AI computing workloads. Here, we review the development of neuro-inspired computing chips, including artificial neural network chips and spiking neural network chips. We propose four key metrics for benchmarking neuro-inspired computing chips - computing density, energy efficiency, computing accuracy, and on-chip learning capability - and discuss co-design principles, from the device to the algorithm level, for neuro-inspired computing chips based on non-volatile memory. We also provide a future electronic design automation tool chain and propose a roadmap for the development of large-scale neuro-inspired computing chips. This Review Article examines the development of neuro-inspired computing chips and their key benchmarking metrics, providing a co-design tool chain and proposing a roadmap for future large-scale chips. | - |
dc.language | English | - |
dc.publisher | NATURE PUBLISHING GROUP | - |
dc.title | Neuro-inspired computing chips | - |
dc.type | Article | - |
dc.identifier.wosid | 000550785800010 | - |
dc.identifier.scopusid | 2-s2.0-85088321938 | - |
dc.type.rims | ART | - |
dc.citation.volume | 3 | - |
dc.citation.issue | 7 | - |
dc.citation.beginningpage | 371 | - |
dc.citation.endingpage | 382 | - |
dc.citation.publicationname | NATURE ELECTRONICS | - |
dc.identifier.doi | 10.1038/s41928-020-0435-7 | - |
dc.contributor.localauthor | Yoo, Hoi-Jun | - |
dc.contributor.nonIdAuthor | Zhang, Wenqiang | - |
dc.contributor.nonIdAuthor | Gao, Bin | - |
dc.contributor.nonIdAuthor | Tang, Jianshi | - |
dc.contributor.nonIdAuthor | Yao, Peng | - |
dc.contributor.nonIdAuthor | Yu, Shimeng | - |
dc.contributor.nonIdAuthor | Chang, Meng-Fan | - |
dc.contributor.nonIdAuthor | Qian, He | - |
dc.contributor.nonIdAuthor | Wu, Huaqiang | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Review | - |
dc.subject.keywordPlus | NETWORKS | - |
dc.subject.keywordPlus | INTELLIGENCE | - |
dc.subject.keywordPlus | PROCESSOR | - |
dc.subject.keywordPlus | INFERENCE | - |
dc.subject.keywordPlus | SYSTEM | - |
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