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Lee, Jongeun
Intelligent Computing and Codesign Lab.
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dc.citation.conferencePlace KO -
dc.citation.conferencePlace Ramada Plaza Jeju HotelJeju -
dc.citation.endPage 78 -
dc.citation.startPage 77 -
dc.citation.title 13th International SoC Design Conference, ISOCC 2016 -
dc.contributor.author Seo, Jungwoo -
dc.contributor.author Yu, Joonsang -
dc.contributor.author Lee, Jongeun -
dc.contributor.author Choi, Kiyoung -
dc.date.accessioned 2023-12-19T20:06:16Z -
dc.date.available 2023-12-19T20:06:16Z -
dc.date.created 2017-02-10 -
dc.date.issued 2016-10-23 -
dc.description.abstract As deep neural networks grow larger, they suffer from a huge number of weights, and thus reducing the overhead of handling those weights becomes one of key challenges nowadays. This paper presents a new approach to binarizing neural networks, where the weights are pruned and forced to take degenerate binary values. Experimental results show that the proposed approach achieves significant reductions in computation and power consumption at the cost of a slight accuracy loss -
dc.identifier.bibliographicCitation 13th International SoC Design Conference, ISOCC 2016, pp.77 - 78 -
dc.identifier.doi 10.1109/ISOCC.2016.7799741 -
dc.identifier.isbn 978-146739308-9 -
dc.identifier.scopusid 2-s2.0-85010369342 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/37535 -
dc.identifier.url http://ieeexplore.ieee.org/document/7799741/authors -
dc.language 영어 -
dc.publisher 13th International SoC Design Conference, ISOCC 2016 -
dc.title A new approach to binarizing neural networks -
dc.type Conference Paper -
dc.date.conferenceDate 2016-10-23 -

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