DC Field | Value | Language |
---|---|---|
dc.contributor.author | Han, Donghyeon | ko |
dc.contributor.author | Lee, Jinsu | ko |
dc.contributor.author | Lee, Jinmook | ko |
dc.contributor.author | Yoo, Hoi-Jun | ko |
dc.date.accessioned | 2019-05-15T13:25:23Z | - |
dc.date.available | 2019-05-15T13:25:23Z | - |
dc.date.created | 2019-05-13 | - |
dc.date.created | 2019-05-13 | - |
dc.date.issued | 2019-05 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, v.66, no.5, pp.1794 - 1804 | - |
dc.identifier.issn | 1549-8328 | - |
dc.identifier.uri | http://hdl.handle.net/10203/261860 | - |
dc.description.abstract | A deep neural network (DNN) online learning processor is proposed with high throughput and low power consumption to achieve real-time object tracking in mobile devices. Four key features enable a low-power DNN online learning. First, a proposed processor is designed with a unified core architecture and it achieves 1.33x higher throughput than the previous state-of-the-art DNN learning processor. Second, the new algorithms, binary feedback alignment (BFA), and dynamic fixed-point based run-length compression (RLC), are proposed and reduce power consumption through the reduction of external memory accesses (EMA). The BFA and dynamic fixed-point-based RLC reduce the EMA by 11.4% and 32.5%, respectively. Third, the new data feeding units, including an integral RLC (iRLC) decoder and a transpose RLC (tRLC) decoder, are co-designed to maximize throughput alongside the proposed algorithms. Finally, a dropout controller in this processor reduces redundant power consumption coming from the unified core and the data feeding architecture by the proposed dynamic clock-gating scheme. This enables the proposed processor to operate DNN online learning with 38.1% lower power consumption. Implemented with 65 nm CMOS technology, the 3.52 mm(2) DNN online learning processor shows 126 mW power consumption and the processor achieves 30.4 frames-per-second throughput in the object tracking application. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | A Low-Power Deep Neural Network Online Learning Processor for Real-Time Object Tracking Application | - |
dc.type | Article | - |
dc.identifier.wosid | 000465305700013 | - |
dc.identifier.scopusid | 2-s2.0-85057824082 | - |
dc.type.rims | ART | - |
dc.citation.volume | 66 | - |
dc.citation.issue | 5 | - |
dc.citation.beginningpage | 1794 | - |
dc.citation.endingpage | 1804 | - |
dc.citation.publicationname | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS | - |
dc.identifier.doi | 10.1109/TCSI.2018.2880363 | - |
dc.contributor.localauthor | Yoo, Hoi-Jun | - |
dc.contributor.nonIdAuthor | Lee, Jinsu | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article; Proceedings Paper | - |
dc.subject.keywordAuthor | Deep neural network | - |
dc.subject.keywordAuthor | online learning | - |
dc.subject.keywordAuthor | object tracking | - |
dc.subject.keywordAuthor | feedback alignment | - |
dc.subject.keywordAuthor | run-length compression | - |
dc.subject.keywordAuthor | dynamic fixed-point representation | - |
dc.subject.keywordAuthor | dropout | - |
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