{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00186501","sets":["1164:2036:9343:9430"]},"path":["9430"],"owner":"11","recid":"186501","title":["カーネルの類似性に基づく近似計算を行うCNNアクセラレータの検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-02-28"},"_buckets":{"deposit":"f7f0853f-2403-4115-b608-358a03dbace7"},"_deposit":{"id":"186501","pid":{"type":"depid","value":"186501","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"カーネルの類似性に基づく近似計算を行うCNNアクセラレータの検討","author_link":["418455","418453","418452","418456","418454"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"カーネルの類似性に基づく近似計算を行うCNNアクセラレータの検討"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"深層学習","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2018-02-28","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"名古屋工業大学"},{"subitem_text_value":"電気通信大学"},{"subitem_text_value":"電気通信大学"},{"subitem_text_value":"名古屋工業大学"},{"subitem_text_value":"電気通信大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Nagoya Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"UEC","subitem_text_language":"en"},{"subitem_text_value":"UEC","subitem_text_language":"en"},{"subitem_text_value":"Nagoya Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"UEC","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/186501/files/IPSJ-SLDM18183031.pdf","label":"IPSJ-SLDM18183031.pdf"},"date":[{"dateType":"Available","dateValue":"2020-02-28"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SLDM18183031.pdf","filesize":[{"value":"410.8 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"10"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"39ec784e-3281-42da-b6ab-0fbe038dc629","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2018 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"進藤, 智司"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松井, 優樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"八巻, 隼人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"津邑, 公暁"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"三輪, 忍"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11451459","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8639","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"画像認識において，Convolutional Neural Network （CNN） と呼ばれるニューラルネットワークが高い認識精度を示し広く利用されている．近年では，認識精度を向上させるために，CNN の規模を増大させる傾向があり，これに伴って，計算時間や消費エネルギーも増大している．この問題に対して，CNN の計算に特化したアクセラレータが開発されている．その中でも，CNN に含まれる計算を近似することで，電力効率の向上を図る研究が盛んに行われている．しかし，CNN の認識精度の低下を抑えつつ，高い電力効率を持つアクセラレータは未だ実現されていない．そこで本研究では，近似計算をアクセレラレータに導入し，認識精度の低下を抑えつつ高電力効率なアクセラレータの実現を目指す．特に，様々な CNN に共通して含まれる代表的なカーネルに特化した回路を予め備えたアクセラレータを検討する．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告システムとLSIの設計技術（SLDM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2018-02-28","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"31","bibliographicVolumeNumber":"2018-SLDM-183"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"updated":"2025-01-20T02:34:32.416736+00:00","created":"2025-01-19T00:53:27.146608+00:00","id":186501}