{"created":"2025-02-21T03:52:35.473727+00:00","id":2007457,"links":{},"metadata":{"_buckets":{"deposit":"2ff4c760-0000-4065-89c3-c807709a0564"},"_deposit":{"created_by":41,"id":"2007457","owners":[41],"pid":{"revision_id":0,"type":"depid","value":"2007457"},"status":"published"},"_oai":{"id":"oai:hiroshima.repo.nii.ac.jp:02007457","sets":["1730444907710"]},"author_link":[],"item_1617186331708":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_title":"Efficient convolution pooling on the GPU","subitem_title_language":"en"}]},"item_1617186419668":{"attribute_name":"Creator","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Suita, Shunsuke","creatorNameLang":"en"}],"familyNames":[{"familyName":"Suita","familyNameLang":"en"}],"givenNames":[{"givenName":"Shunsuke","givenNameLang":"en"}]},{"creatorNames":[{"creatorName":"Nishimura, Takahiro","creatorNameLang":"en"}],"familyNames":[{"familyName":"Nishimura","familyNameLang":"en"}],"givenNames":[{"givenName":"Takahiro","givenNameLang":"en"}]},{"creatorNames":[{"creatorName":"Tokura, Hiroki","creatorNameLang":"en"}],"familyNames":[{"familyName":"Tokura","familyNameLang":"en"}],"givenNames":[{"givenName":"Hiroki","givenNameLang":"en"}]},{"creatorNames":[{"creatorName":"Nakano, Koji","creatorNameLang":"en"}],"familyNames":[{"familyName":"Nakano","familyNameLang":"en"}],"givenNames":[{"givenName":"Koji","givenNameLang":"en"}]},{"creatorNames":[{"creatorName":"Itou, Yasuaki","creatorNameLang":"en"}],"familyNames":[{"familyName":"Itou","familyNameLang":"en"}],"givenNames":[{"givenName":"Yasuaki","givenNameLang":"en"}]},{"creatorNames":[{"creatorName":"Kasagi, Akihiko","creatorNameLang":"en"}],"familyNames":[{"familyName":"Kasagi","familyNameLang":"en"}],"givenNames":[{"givenName":"Akihiko","givenNameLang":"en"}]},{"creatorNames":[{"creatorName":"Tabaru, Tsuguchika","creatorNameLang":"en"}],"familyNames":[{"familyName":"Tabaru","familyNameLang":"en"}],"givenNames":[{"givenName":"Tsuguchika","givenNameLang":"en"}]}]},"item_1617186476635":{"attribute_name":"Access Rights","attribute_value_mlt":[{"subitem_access_right":"open access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_abf2"}]},"item_1617186499011":{"attribute_name":"Rights","attribute_value_mlt":[{"subitem_rights":"© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/"},{"subitem_rights":"This is not the published version. Please cite only the published version. この論文は出版社版ではありません。引用の際には出版社版をご確認、ご利用ください。"}]},"item_1617186609386":{"attribute_name":"Subject","attribute_value_mlt":[{"subitem_subject":"Deep learning","subitem_subject_scheme":"Other"},{"subitem_subject":"Neural Networks","subitem_subject_scheme":"Other"},{"subitem_subject":"Convolution","subitem_subject_scheme":"Other"},{"subitem_subject":"Average pooling","subitem_subject_scheme":"Other"},{"subitem_subject":"GPU","subitem_subject_scheme":"Other"}]},"item_1617186626617":{"attribute_name":"Description","attribute_value_mlt":[{"subitem_description":"The main contribution of this paper is to show efficient implementations of the convolution-pooling in the GPU, in which the pooling follows the multiple convolution. Since the multiple convolution and the pooling operations are performed alternately in earlier stages of many Convolutional Neural Networks (CNNs), it is very important to accelerate the convolution-pooling. Our new GPU implementation uses two techniques, (1) convolution interchange with direct sum, and (2) conversion to matrix multiplication. By these techniques, the computational and memory access cost are reduced. Further the convolution interchange is converted to matrix multiplication, which can be computed by cuBLAS very efficiently. Experimental results using Tesla V100 GPU show that our new GPU implementation compatible with cuDNN for the convolution-pooling is expected 2.90 times and 1.43 times faster for fp32 and fp16 than the multiple convolution and then the pooling by cuDNN, respectively. the most popular library of primitives to implement the CNNs in the GPU.","subitem_description_language":"en"}]},"item_1617186643794":{"attribute_name":"Publisher","attribute_value_mlt":[{"subitem_publisher":"Elsevier"}]},"item_1617186702042":{"attribute_name":"Language","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_1617186920753":{"attribute_name":"Source Identifier","attribute_value_mlt":[{"subitem_source_identifier":"0743-7315","subitem_source_identifier_type":"ISSN"}]},"item_1617187024783":{"attribute_name":"Page Start","attribute_value_mlt":[{"subitem_start_page":"222"}]},"item_1617187056579":{"attribute_name":"Bibliographic Information","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2020-04","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"229","bibliographicPageStart":"222","bibliographicVolumeNumber":"138","bibliographic_titles":[{"bibliographic_title":"Journal of Parallel and Distributed Computing"},{"bibliographic_title":"Journal of Parallel and Distributed Computing"}]}]},"item_1617258105262":{"attribute_name":"Resource Type","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_1617265215918":{"attribute_name":"Version Type","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_b1a7d7d4d402bcce","subitem_version_type":"AO"}]},"item_1617353299429":{"attribute_name":"Relation","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1016/j.jpdc.2019.12.006","subitem_relation_type_select":"DOI"}},{"subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.1016/j.jpdc.2019.12.006","subitem_relation_type_select":"DOI"}}]},"item_1617605131499":{"attribute_name":"File","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2022-05-01"}],"displaytype":"simple","fileDate":[{"fileDateType":"Available","fileDateValue":"2022-05-01"}],"filename":"JPDC_138_222.pdf","filesize":[{"value":"126.3 KB"}],"mimetype":"application/pdf","url":{"objectType":"fulltext","url":"https://hiroshima.repo.nii.ac.jp/record/2007457/files/JPDC_138_222.pdf"},"version_id":"5b4087a1-9dfb-43a6-b199-a6c614bfc487"}]},"item_1732771732025":{"attribute_name":"旧ID","attribute_value":"50422"},"item_1732772494514":{"attribute_name":"備考","attribute_value":"Post-print version/PDF may be used in an institutional repository after an embargo period of 24 months."},"item_title":"Efficient convolution pooling on the GPU","item_type_id":"40003","owner":"41","path":["1730444907710"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2023-03-18"},"publish_date":"2023-03-18","publish_status":"0","recid":"2007457","relation_version_is_last":true,"title":["Efficient convolution pooling on the GPU"],"weko_creator_id":"41","weko_shared_id":-1},"updated":"2025-02-21T09:59:09.062407+00:00"}