{"links":{},"id":227104,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00227104","sets":["1164:1579:11081:11309"]},"path":["11309"],"owner":"44499","recid":"227104","title":["リニアアレイ型CGRA向けCGN実装方法の検討と評価"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-07-27"},"_buckets":{"deposit":"c5821fb9-919b-4bd3-8ced-e6e67bd9d5da"},"_deposit":{"id":"227104","pid":{"type":"depid","value":"227104","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"リニアアレイ型CGRA向けCGN実装方法の検討と評価","author_link":["604498","604497"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"リニアアレイ型CGRA向けCGN実装方法の検討と評価"}]},"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":"2023-07-27","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"奈良先端科学技術大学院大学先端科学技術研究科"},{"subitem_text_value":"奈良先端科学技術大学院大学先端科学技術研究科"}]},"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/227104/files/IPSJ-ARC23254027.pdf","label":"IPSJ-ARC23254027.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-ARC23254027.pdf","filesize":[{"value":"993.1 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"16"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"15c6d3c6-5e0d-4015-b14f-bb0c09a30678","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 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":"金, 道鉉"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中島, 康彦"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10096105","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":"2188-8574","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"GCN はグラフデータを分析するためのニューラルネットワークモデルの一種である．GCN は，分子やタンパク質の作用予測や，商品データの分析など，グラフ化可能な様々な場面で活用されている．GCN のための様々なアクセラレータが開発されているが，プログラマビリティが低く，他のモデルでの使用は困難な場合がある．CGRAは，専用アクセラレータとは違い，他モデル，他アルゴリズムの駆動が可能であり，プログラミング可能でありながらデータを演算ユニットに直接引き渡すことから高電力効率の達成が可能であるとされている．本研究では，GCN の主な演算である疎行列-密行列積 (SpMM) をリニアアレイ型 CGRA の IMAX2 での実装方法を検討し，性能評価を行った．その結果，GCN に用いられるグラフデータに近い形の疎行列において，GPU 等に比べ高い面積対性能を達成し， GCN が IMAX2 において高効率に動作する可能性を示せた．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"5","bibliographic_titles":[{"bibliographic_title":"研究報告システム・アーキテクチャ（ARC）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2023-07-27","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"27","bibliographicVolumeNumber":"2023-ARC-254"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:26:25.124816+00:00","updated":"2025-01-19T12:16:35.145519+00:00"}