Custom Sub-Systems and Circuits for Deep Learning: Guest Editorial Overview
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Chen, Chia-Yu | ko |
| dc.contributor.author | Murmann, Boris | ko |
| dc.contributor.author | Seo, Jae-Sun | ko |
| dc.contributor.author | Yoo, Hoi-Jun | ko |
| dc.date.accessioned | 2019-07-08T08:10:04Z | - |
| dc.date.available | 2019-07-08T08:10:04Z | - |
| dc.date.created | 2019-07-01 | - |
| dc.date.created | 2019-07-01 | - |
| dc.date.issued | 2019-06 | - |
| dc.identifier.citation | IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, v.9, no.2, pp.247 - 252 | - |
| dc.identifier.issn | 2156-3357 | - |
| dc.identifier.uri | http://hdl.handle.net/10203/263126 | - |
| dc.description.abstract | This survey paper summarizes recent progress of deep learning circuits and systems technologies and contains four topics: hardware-centric deep learning algorithms, digital architectures, analog architectures, and system demonstrations. We present an overview of these four areas and introduce key contributions of papers in this special issue. | - |
| dc.language | English | - |
| dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
| dc.title | Custom Sub-Systems and Circuits for Deep Learning: Guest Editorial Overview | - |
| dc.type | Article | - |
| dc.identifier.wosid | 000471693500001 | - |
| dc.identifier.scopusid | 2-s2.0-85067000225 | - |
| dc.type.rims | ART | - |
| dc.citation.volume | 9 | - |
| dc.citation.issue | 2 | - |
| dc.citation.beginningpage | 247 | - |
| dc.citation.endingpage | 252 | - |
| dc.citation.publicationname | IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS | - |
| dc.identifier.doi | 10.1109/JETCAS.2019.2918317 | - |
| dc.contributor.localauthor | Yoo, Hoi-Jun | - |
| dc.contributor.nonIdAuthor | Chen, Chia-Yu | - |
| dc.contributor.nonIdAuthor | Murmann, Boris | - |
| dc.contributor.nonIdAuthor | Seo, Jae-Sun | - |
| dc.description.isOpenAccess | N | - |
| dc.type.journalArticle | Editorial Material | - |
| dc.subject.keywordAuthor | Deep learning | - |
| dc.subject.keywordAuthor | machine learning | - |
| dc.subject.keywordAuthor | accelerators | - |
| dc.subject.keywordAuthor | computer architectures | - |
| dc.subject.keywordAuthor | integrated circuit designs | - |
| dc.subject.keywordAuthor | neural network hardware | - |
| dc.subject.keywordAuthor | in-memory computing | - |
| dc.subject.keywordAuthor | data-flow architectures | - |
| dc.subject.keywordAuthor | reduced-precision | - |
| dc.subject.keywordAuthor | approximate computing | - |
| dc.subject.keywordAuthor | distributed learning | - |
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