{"id":"https://openalex.org/W4379115862","doi":"https://doi.org/10.23919/date56975.2023.10137121","title":"CEST: Computation-Efficient N:M Sparse Training for Deep Neural Networks","display_name":"CEST: Computation-Efficient N:M Sparse Training for Deep Neural Networks","publication_year":2023,"publication_date":"2023-04-01","ids":{"openalex":"https://openalex.org/W4379115862","doi":"https://doi.org/10.23919/date56975.2023.10137121"},"language":"en","primary_location":{"id":"doi:10.23919/date56975.2023.10137121","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date56975.2023.10137121","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066888122","display_name":"Chao Fang","orcid":"https://orcid.org/0000-0003-3430-1189"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Fang","raw_affiliation_strings":["Nanjing University,ICAIS Lab","ICAIS Lab, Nanjing University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University,ICAIS Lab","institution_ids":["https://openalex.org/I881766915"]},{"raw_affiliation_string":"ICAIS Lab, Nanjing University","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101618534","display_name":"Wei Sun","orcid":"https://orcid.org/0000-0002-0317-583X"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Wei Sun","raw_affiliation_strings":["Eindhoven University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041431476","display_name":"Aojun Zhou","orcid":"https://orcid.org/0000-0002-4742-8624"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Aojun Zhou","raw_affiliation_strings":["CUHK,CUHK-Sensetime Joint Lab","CUHK-Sensetime Joint Lab, CUHK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CUHK,CUHK-Sensetime Joint Lab","institution_ids":["https://openalex.org/I889458895"]},{"raw_affiliation_string":"CUHK-Sensetime Joint Lab, CUHK","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100696999","display_name":"Zhongfeng Wang","orcid":"https://orcid.org/0000-0002-7227-4786"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongfeng Wang","raw_affiliation_strings":["Nanjing University,ICAIS Lab","ICAIS Lab, Nanjing University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University,ICAIS Lab","institution_ids":["https://openalex.org/I881766915"]},{"raw_affiliation_string":"ICAIS Lab, Nanjing University","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7283,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.78867327,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7257879376411438},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6951608657836914},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6914674043655396},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6022390723228455},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48785972595214844},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4825267493724823},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.4529324173927307},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3900224566459656},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.3371660113334656},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3239400088787079}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7257879376411438},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6951608657836914},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6914674043655396},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6022390723228455},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48785972595214844},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4825267493724823},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.4529324173927307},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3900224566459656},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.3371660113334656},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3239400088787079},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/date56975.2023.10137121","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date56975.2023.10137121","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G174380750","display_name":null,"funder_award_id":"62174084,62104097","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2787094505","https://openalex.org/W2990001850","https://openalex.org/W3098417701","https://openalex.org/W3112412839","https://openalex.org/W3127067080","https://openalex.org/W3211907200","https://openalex.org/W4213446428","https://openalex.org/W4287327026","https://openalex.org/W4291653336","https://openalex.org/W4312929503","https://openalex.org/W6789946416","https://openalex.org/W6790943123","https://openalex.org/W6803607599"],"related_works":["https://openalex.org/W2373300491","https://openalex.org/W1212596013","https://openalex.org/W2378744544","https://openalex.org/W2594301978","https://openalex.org/W2379704676","https://openalex.org/W4206442282","https://openalex.org/W2384505857","https://openalex.org/W2136583354","https://openalex.org/W2355171581","https://openalex.org/W4229439743"],"abstract_inverted_index":{"N:M":[0,42,56,99],"fine-grained":[1],"structured":[2],"sparsity":[3,11],"has":[4,28],"attracted":[5],"attention":[6],"due":[7],"to":[8,19,72,90],"its":[9],"practical":[10],"ratio":[12],"and":[13,33,98],"hardware-friendly":[14],"pattern.":[15],"However,":[16],"the":[17,75,94,108,112],"potential":[18],"accelerate":[20],"N:m":[21],"sparse":[22,43,57,83,100],"deep":[23],"neural":[24],"network":[25],"(DNN)":[26],"training":[27,109],"not":[29],"been":[30],"fully":[31],"exploited,":[32],"there":[34],"is":[35,69,87],"a":[36,52],"lack":[37],"of":[38],"efficient":[39],"hardware":[40],"supporting":[41],"training.":[44],"To":[45],"tackle":[46],"these":[47],"challenges,":[48],"this":[49],"paper":[50],"presents":[51],"computation-efficient":[53],"scheme":[54],"for":[55],"DNN":[58],"training,":[59],"called":[60],"CEST.":[61],"A":[62,82],"bidirectional":[63],"weight":[64],"pruning":[65],"method,":[66],"dubbed":[67],"BDWP,":[68],"firstly":[70],"proposed":[71],"significantly":[73,106],"reduce":[74],"computational":[76],"cost":[77],"while":[78],"maintaining":[79],"model":[80],"accuracy.":[81],"accelerator,":[84],"namely":[85],"SAT,":[86],"further":[88],"developed":[89],"neatly":[91],"support":[92],"both":[93],"regular":[95],"dense":[96],"operations":[97],"operations.":[101],"Experimental":[102],"results":[103],"show":[104],"CEST":[105],"improves":[107],"throughput":[110],"by$1.89-12.49\\times$and":[111],"energy":[113],"efficiency":[114],"by$1.86-2.76\\times$.":[115]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}