{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00186045","sets":["1164:2240:9411:9412"]},"path":["9412"],"owner":"11","recid":"186045","title":["CNNの学習におけるチャネル方向並列化の提案"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-02-21"},"_buckets":{"deposit":"89da691b-b650-4af8-98ab-a1d0c62781bb"},"_deposit":{"id":"186045","pid":{"type":"depid","value":"186045","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"CNNの学習におけるチャネル方向並列化の提案","author_link":["415801","415803","415802","415806","415804","415805"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"CNNの学習におけるチャネル方向並列化の提案"},{"subitem_title":"Proposal of a channel-wise parallelization scheme for training of CNN","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"機械学習","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2018-02-21","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京大学大学院工学系研究科／産業技術総合研究所"},{"subitem_text_value":"産業技術総合研究所"},{"subitem_text_value":"東京大学情報基盤センター"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Engineering, The University of Tokyo / National Institute of Advanced Industrial Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Advanced Industrial Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Information Technology Center, The University of Tokyo","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/186045/files/IPSJ-HPC18163016.pdf","label":"IPSJ-HPC18163016.pdf"},"date":[{"dateType":"Available","dateValue":"2020-02-21"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-HPC18163016.pdf","filesize":[{"value":"684.5 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Akanuma","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ryousei, Takano","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tomohiro, Kudoh","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10463942","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":"2188-8841","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"CNN は現在画像認識をはじめとしたさまざまな処理に用いられているが，その学習には膨大な時間が必要である．そこで処理をデータ並列で並列化による CNN の学習高速化が一般に行われている．しかしこの方式では利用できる並列度がグローバルミニバッチサイズに制限されてしまう．この制限を拡張するために本研究ではデータ並列と CNN のチャネル方向の並列化を導入したモデル並列を併用したハイブリッド並列を提案する．演算時間の予想モデルを構築しデータ並列とハイブリッド並列の比較を行った．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"7","bibliographic_titles":[{"bibliographic_title":"研究報告ハイパフォーマンスコンピューティング（HPC）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2018-02-21","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"16","bibliographicVolumeNumber":"2018-HPC-163"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":186045,"updated":"2025-01-20T02:44:14.070104+00:00","links":{},"created":"2025-01-19T00:53:06.175351+00:00"}