{"updated":"2025-01-19T15:56:35.924671+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00216068","sets":["1164:2036:10820:10821"]},"path":["10821"],"owner":"44499","recid":"216068","title":["蒸留とレイヤー枝刈りによるエッジデバイス推論処理の高速化について"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-01-17"},"_buckets":{"deposit":"6662038e-9f5a-45a0-9e7b-5be17ddfd08b"},"_deposit":{"id":"216068","pid":{"type":"depid","value":"216068","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"蒸留とレイヤー枝刈りによるエッジデバイス推論処理の高速化について","author_link":["557229","557230","557226","557225","557231","557228","557227","557232"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"蒸留とレイヤー枝刈りによるエッジデバイス推論処理の高速化について"},{"subitem_title":"Accelerating Deep Neural Networks on Edge Devices by Knowledge Distillation and Layer Pruning","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ニューラルネットワーク","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2022-01-17","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京工業大学"},{"subitem_text_value":"東京工業大学"},{"subitem_text_value":"東京工業大学"},{"subitem_text_value":"東京工業大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Tokyo Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Tokyo Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Tokyo Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Tokyo Institute of Technology","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/216068/files/IPSJ-SLDM22197011.pdf","label":"IPSJ-SLDM22197011.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SLDM22197011.pdf","filesize":[{"value":"1.8 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"10"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"31b85e83-d0f3-419d-a00d-2c2bc9710708","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"市川, 雄樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"神宮司, 明良"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"倉持, 亮佑"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中原, 啓貴"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yuki, Ichikawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Akira, Jinguji","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ryosuke, Kuramochi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroki, Nakahara","creatorNameLang":"en"}],"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":"Deep Neural Network (DNN) はパラメータ数や計算量が多く，計算資源の限られるエッジデバイスでの活用は難しい．したがって蒸留や枝刈りといった DNN の軽量化手法が提案されている．提案手法はこれらを用いて既存の訓練済みモデルを効率的に軽量化する．この手法により，短時間でモデルをエッジデバイス向けに軽量化できることを示す．また Jetson Nano と DPU を用いて，認識精度と推論速度のトレードオフを明らかにする．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"A deep neural network (DNN) is computationally expensive, making it challenging to run DNN on edge devices. Therefore, model compression techniques such as knowledge distillation and pruning are proposed. This research suggests an efficient method to compress pretrained models using these techniques. We show that our method can compress models for edge devices in a short time. We also show a trade–off between recognition accuracy and inference time on Jetson Nano GPU and DPU on a Xilinx FPGA.","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":"2022-01-17","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"11","bibliographicVolumeNumber":"2022-SLDM-197"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:16:47.734067+00:00","id":216068,"links":{}}