A study on supporting spiking neural network models based on multiple neuromorphic processors
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hong B. | - |
dc.contributor.author | Cho J. | - |
dc.contributor.author | Kim B. | - |
dc.contributor.author | Min H. | - |
dc.contributor.author | Hong J. | - |
dc.contributor.author | Lee K.M. | - |
dc.date.available | 2020-02-20T03:20:03Z | - |
dc.date.created | 2020-02-18 | - |
dc.date.issued | 2019-09 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/35492 | - |
dc.description.abstract | Neuromorphic computing or neuromorphic engineering is an engineering discipline that attempts to simulate human brain function by creating circuits that mimic the shape of neurons. In the field of neuromorphic computing, neuromorphic processors are used. There are many types of neuromorphic processors, and there are neuromorphic processors implemented based on FPGAS. Neuromorphic processors use an artificial intelligence model called a spiking neural network. Each neuromorphic processor has different characteristics. For example, the spiking neural network model supported by each supported neuromorphic processor may be different. In this paper, we propose a service architecture named NAAL (Neuromorphic Architecture Abstract Layer) that enables the use of spiking neural networks by virtualizing various neuromorphic processors with different characteristics. © 2019 Association for Computing Machinery. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.relation.isPartOf | Proceedings of the 2019 Research in Adaptive and Convergent Systems, RACS 2019 | - |
dc.title | A study on supporting spiking neural network models based on multiple neuromorphic processors | - |
dc.type | Article | - |
dc.identifier.doi | 10.1145/3338840.3355692 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | Proceedings of the 2019 Research in Adaptive and Convergent Systems, RACS 2019, pp.131 - 132 | - |
dc.description.journalClass | 3 | - |
dc.identifier.scopusid | 2-s2.0-85077196214 | - |
dc.citation.endPage | 132 | - |
dc.citation.startPage | 131 | - |
dc.citation.title | Proceedings of the 2019 Research in Adaptive and Convergent Systems, RACS 2019 | - |
dc.contributor.affiliatedAuthor | Hong J. | - |
dc.type.docType | Conference Paper | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordAuthor | Neuromorphic computing | - |
dc.subject.keywordAuthor | Neuromorphic porcessor | - |
dc.subject.keywordAuthor | Spiking neural networks | - |
dc.subject.keywordPlus | Brain | - |
dc.subject.keywordPlus | Network architecture | - |
dc.subject.keywordPlus | Engineering disciplines | - |
dc.subject.keywordPlus | Human brain functions | - |
dc.subject.keywordPlus | Neuromorphic | - |
dc.subject.keywordPlus | Neuromorphic Architectures | - |
dc.subject.keywordPlus | Neuromorphic computing | - |
dc.subject.keywordPlus | Neuromorphic engineering | - |
dc.subject.keywordPlus | Service architecture | - |
dc.subject.keywordPlus | Spiking neural networks | - |
dc.subject.keywordPlus | Multilayer neural networks | - |
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