{"id":"https://openalex.org/W2996874060","doi":"https://doi.org/10.1145/3373376.3378534","title":"PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning","display_name":"PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning","publication_year":2020,"publication_date":"2020-03-09","ids":{"openalex":"https://openalex.org/W2996874060","doi":"https://doi.org/10.1145/3373376.3378534","mag":"2996874060"},"language":"en","primary_location":{"id":"doi:10.1145/3373376.3378534","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3373376.3378534","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3373376.3378534","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3373376.3378534","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Wei Niu","orcid":null},"institutions":[{"id":"https://openalex.org/I16285277","display_name":"William & Mary","ror":"https://ror.org/03hsf0573","country_code":"US","type":"education","lineage":["https://openalex.org/I16285277"]},{"id":"https://openalex.org/I267592682","display_name":"Williams (United States)","ror":"https://ror.org/007zhvp17","country_code":"US","type":"company","lineage":["https://openalex.org/I267592682"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wei Niu","raw_affiliation_strings":["College of William and Mary, Williamsburg, VA, USA"],"affiliations":[{"raw_affiliation_string":"College of William and Mary, Williamsburg, VA, USA","institution_ids":["https://openalex.org/I16285277","https://openalex.org/I267592682"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiaolong Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaolong Ma","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Sheng Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sheng Lin","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Shihao Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shihao Wang","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xuehai Qian","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xuehai Qian","raw_affiliation_strings":["University of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xue Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xue Lin","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yanzhi Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanzhi Wang","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"last","author":{"id":null,"display_name":"Bin Ren","orcid":null},"institutions":[{"id":"https://openalex.org/I16285277","display_name":"William & Mary","ror":"https://ror.org/03hsf0573","country_code":"US","type":"education","lineage":["https://openalex.org/I16285277"]},{"id":"https://openalex.org/I267592682","display_name":"Williams (United States)","ror":"https://ror.org/007zhvp17","country_code":"US","type":"company","lineage":["https://openalex.org/I267592682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bin Ren","raw_affiliation_strings":["College of William and Mary, Williamsburg, VA, USA"],"affiliations":[{"raw_affiliation_string":"College of William and Mary, Williamsburg, VA, USA","institution_ids":["https://openalex.org/I16285277","https://openalex.org/I267592682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I16285277","https://openalex.org/I267592682"],"apc_list":null,"apc_paid":null,"fwci":23.4595,"has_fulltext":false,"cited_by_count":206,"citation_normalized_percentile":{"value":0.99694825,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"907","last_page":"922"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9959999918937683,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.8695999979972839},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.6991999745368958},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6773999929428101},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5536999702453613},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5389999747276306},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5218999981880188}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.8695999979972839},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7639999985694885},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.6991999745368958},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6773999929428101},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5536999702453613},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5389999747276306},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5218999981880188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44519999623298645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31859999895095825},{"id":"https://openalex.org/C95491727","wikidata":"https://www.wikidata.org/wiki/Q992968","display_name":"Mobile telephony","level":3,"score":0.3077000081539154},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3075999915599823},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2937999963760376},{"id":"https://openalex.org/C144543869","wikidata":"https://www.wikidata.org/wiki/Q2738570","display_name":"Mobile computing","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3373376.3378534","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3373376.3378534","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3373376.3378534","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2001.00138","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2001.00138","pdf_url":"https://arxiv.org/pdf/2001.00138","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3373376.3378534","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3373376.3378534","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3373376.3378534","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1332542428","display_name":"RTML: Large: Efficient and Adaptive Real-Time Learning for Next Generation Wireless Systems","funder_award_id":"1937500","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2587219159","display_name":"SHF: Medium: Collaborative Research: ADMM-NN: A Unified Software/Hardware Framework of DNN Computation and Storage Reduction Using ADMM","funder_award_id":"1901378","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4856824351","display_name":null,"funder_award_id":"1739748","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5458957665","display_name":null,"funder_award_id":"CCF-1919289","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6092014333","display_name":"SPX: Collaborative Research: FASTLEAP: FPGA based compact Deep Learning Platform","funder_award_id":"1919289","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7217889279","display_name":null,"funder_award_id":"CNS-1739748","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7940815196","display_name":null,"funder_award_id":"CCF-1937500","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G818952299","display_name":null,"funder_award_id":"CNS-1739748,CCF-1937500,CCF-1919117,CCF-1901378,CCF-1919289","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8587235517","display_name":"SPX: Collaborative Research: FASTLEAP: FPGA based compact Deep Learning Platform","funder_award_id":"1919117","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8669643791","display_name":null,"funder_award_id":"CCF-1919117","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8912281322","display_name":null,"funder_award_id":"CCF-1901378","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2996874060.pdf","grobid_xml":"https://content.openalex.org/works/W2996874060.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W566555209","https://openalex.org/W1977295820","https://openalex.org/W1997430507","https://openalex.org/W2016053056","https://openalex.org/W2035674734","https://openalex.org/W2058616551","https://openalex.org/W2093866254","https://openalex.org/W2130325614","https://openalex.org/W2133025798","https://openalex.org/W2172654076","https://openalex.org/W2194775991","https://openalex.org/W2233116163","https://openalex.org/W2295652899","https://openalex.org/W2297325673","https://openalex.org/W2300242332","https://openalex.org/W2337546824","https://openalex.org/W2347043188","https://openalex.org/W2468875367","https://openalex.org/W2553915786","https://openalex.org/W2625457103","https://openalex.org/W2626129225","https://openalex.org/W2739601332","https://openalex.org/W2748818695","https://openalex.org/W2765315405","https://openalex.org/W2804032941","https://openalex.org/W2860338957","https://openalex.org/W2889402930","https://openalex.org/W2962818002","https://openalex.org/W2962883549","https://openalex.org/W2963163009","https://openalex.org/W2963363373","https://openalex.org/W3001665736","https://openalex.org/W6600194071","https://openalex.org/W7055713322"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,35,63],"emergence":[2],"of":[3,6,52],"a":[4],"spectrum":[5],"high-end":[7],"mobile":[8],"devices,":[9],"many":[10],"applications":[11],"that":[12],"formerly":[13],"required":[14],"desktop-level":[15],"computation":[16,37],"capability":[17],"are":[18],"being":[19],"transferred":[20],"to":[21],"these":[22],"devices.":[23],"However,":[24],"executing":[25],"Deep":[26],"Neural":[27],"Networks":[28],"(DNNs)":[29],"inference":[30],"is":[31,48,54,68,77],"still":[32],"challenging":[33],"considering":[34],"high":[36,46],"and":[38],"storage":[39],"demands,":[40],"specifically,":[41],"if":[42],"real-time":[43],"performance":[44],"with":[45,81],"accuracy":[47,83],"needed.":[49],"Weight":[50],"pruning":[51,67,76],"DNNs":[53],"proposed,":[55],"but":[56,71,80],"existing":[57],"schemes":[58],"represent":[59],"two":[60],"extremes":[61],"in":[62],"design":[64],"space:":[65],"non-structured":[66],"fine-grained,":[69],"accurate,":[70],"not":[72],"hardware":[73],"friendly;":[74],"structured":[75],"coarse-grained,":[78],"hardware-efficient,":[79],"higher":[82],"loss.":[84]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":30},{"year":2024,"cited_by_count":30},{"year":2023,"cited_by_count":50},{"year":2022,"cited_by_count":50},{"year":2021,"cited_by_count":40},{"year":2020,"cited_by_count":5}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2020-01-10T00:00:00"}