{"created":"2025-01-19T01:19:46.407551+00:00","updated":"2025-01-19T14:48:14.481997+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00219709","sets":["6164:6165:6640:11008"]},"path":["11008"],"owner":"44499","recid":"219709","title":["Multi-scale Attentionを用いた物体検出アルゴリズムのFPGA実装"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-07-06"},"_buckets":{"deposit":"385d1066-124b-45bb-8f33-1bf0c879ee95"},"_deposit":{"id":"219709","pid":{"type":"depid","value":"219709","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Multi-scale Attentionを用いた物体検出アルゴリズムのFPGA実装","author_link":["573329","573326","573328","573327"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Multi-scale Attentionを用いた物体検出アルゴリズムのFPGA実装"}]},"item_type_id":"18","publish_date":"2022-07-06","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"(株)東芝研究開発センターIOTエッジラボラトリー"},{"subitem_text_value":"(株)東芝研究開発センターIOTエッジラボラトリー"},{"subitem_text_value":"(株)東芝 研究開発センター メディアAIラボラトリー"},{"subitem_text_value":"(株)東芝 研究開発センター メディアAIラボラトリー"}]},"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/219709/files/IPSJ-DICOMO2022135.pdf","label":"IPSJ-DICOMO2022135.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-06"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DICOMO2022135.pdf","filesize":[{"value":"1.6 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"99356f3d-eba4-49f8-9970-04c1af28db51","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"古田, 雅則"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"坂, 耕一郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小林, 大祐"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"柴田, 智行"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"防犯やインフラの状態監視に向けたクラウド型監視カメラシステムでは，利用エリアの拡大や膨大な画像データの削減を目的として，エッジ側であるカメラ内への物体検出アルゴリズムの組込みニーズが上昇している．本稿では，CNN を用いた物体検出アルゴリズムに着目し，FPGA を用いたエッジ AI アクセラレータの設計方法を提案する．Self-Attention 技術を用いた少ない演算量の物体検出アルゴリズムと，高速なスループットを省電力で実現する FPGA 回路設計技術を開発．エッジ AI アクセラレータのプロトタイプ試作を行い，79.3% の物体検出精度，824GOPs の演算量，77.7msec の物体検出演算処理スループットの性能を実機による評価で確認した．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"975","bibliographic_titles":[{"bibliographic_title":"マルチメディア，分散，協調とモバイルシンポジウム2022論文集"}],"bibliographicPageStart":"968","bibliographicIssueDates":{"bibliographicIssueDate":"2022-07-06","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2022"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":219709,"links":{}}