{"id":"https://openalex.org/W2990396458","doi":"https://doi.org/10.1109/smc.2019.8914081","title":"Convolution Acceleration: Query Based Filter Pruning","display_name":"Convolution Acceleration: Query Based Filter Pruning","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2990396458","doi":"https://doi.org/10.1109/smc.2019.8914081","mag":"2990396458"},"language":"en","primary_location":{"id":"doi:10.1109/smc.2019.8914081","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2019.8914081","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"article","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/A5088993778","display_name":"Arthur Feeney","orcid":"https://orcid.org/0009-0006-0011-3461"},"institutions":[{"id":"https://openalex.org/I60060512","display_name":"Trinity University","ror":"https://ror.org/00t8gz605","country_code":"US","type":"education","lineage":["https://openalex.org/I60060512"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Arthur Feeney","raw_affiliation_strings":["Department of Computer Science, Trinity University, One Trinity Place, San Antonio, TX, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Trinity University, One Trinity Place, San Antonio, TX, USA","institution_ids":["https://openalex.org/I60060512"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100433508","display_name":"Yu Zhang","orcid":"https://orcid.org/0000-0002-2052-2231"},"institutions":[{"id":"https://openalex.org/I60060512","display_name":"Trinity University","ror":"https://ror.org/00t8gz605","country_code":"US","type":"education","lineage":["https://openalex.org/I60060512"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yu Zhang","raw_affiliation_strings":["Department of Computer Science, Trinity University, One Trinity Place, San Antonio, TX, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Trinity University, One Trinity Place, San Antonio, TX, USA","institution_ids":["https://openalex.org/I60060512"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5088993778"],"corresponding_institution_ids":["https://openalex.org/I60060512"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13580385,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"abs 1409 1556","issue":null,"first_page":"1645","last_page":"1652"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":1.0,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":1.0,"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/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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9984999895095825,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7819792032241821},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.7466779947280884},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6894079446792603},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6826057434082031},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.517187237739563},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4358447790145874},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.43439897894859314},{"id":"https://openalex.org/keywords/locality","display_name":"Locality","score":0.4300921857357025},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.4195294976234436},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.40809962153434753},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3965778946876526},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39639487862586975},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34049397706985474},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.10265415906906128}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7819792032241821},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7466779947280884},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6894079446792603},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6826057434082031},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.517187237739563},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4358447790145874},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.43439897894859314},{"id":"https://openalex.org/C2779808786","wikidata":"https://www.wikidata.org/wiki/Q6664603","display_name":"Locality","level":2,"score":0.4300921857357025},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.4195294976234436},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.40809962153434753},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3965778946876526},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39639487862586975},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34049397706985474},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.10265415906906128},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc.2019.8914081","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2019.8914081","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4300000071525574,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1502916507","https://openalex.org/W1575370549","https://openalex.org/W1686810756","https://openalex.org/W1996901117","https://openalex.org/W2012833704","https://openalex.org/W2038276547","https://openalex.org/W2114766824","https://openalex.org/W2118323718","https://openalex.org/W2126754439","https://openalex.org/W2147717514","https://openalex.org/W2156150815","https://openalex.org/W2162006472","https://openalex.org/W2163605009","https://openalex.org/W2169054943","https://openalex.org/W2170037597","https://openalex.org/W2279057335","https://openalex.org/W2570467259","https://openalex.org/W2707890836","https://openalex.org/W2754249189","https://openalex.org/W2787884921","https://openalex.org/W2899771611","https://openalex.org/W2949067151","https://openalex.org/W2950967261","https://openalex.org/W2962835968","https://openalex.org/W2962863202","https://openalex.org/W2962965870","https://openalex.org/W2963056065","https://openalex.org/W2963287528","https://openalex.org/W2963703787","https://openalex.org/W2964350391","https://openalex.org/W3118608800","https://openalex.org/W3143293593","https://openalex.org/W4230940751","https://openalex.org/W6629956336","https://openalex.org/W6634599061","https://openalex.org/W6637373629","https://openalex.org/W6677103964","https://openalex.org/W6677953180","https://openalex.org/W6678637124","https://openalex.org/W6684191040","https://openalex.org/W6684813226","https://openalex.org/W6685012337","https://openalex.org/W6694260854","https://openalex.org/W6726275242","https://openalex.org/W6739917289","https://openalex.org/W6755145148","https://openalex.org/W6756040250","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W1556451512","https://openalex.org/W1555349535","https://openalex.org/W4234091740","https://openalex.org/W4213350282","https://openalex.org/W2230171082","https://openalex.org/W2583128298","https://openalex.org/W2022275305","https://openalex.org/W2369125128","https://openalex.org/W2134423494","https://openalex.org/W1586728173"],"abstract_inverted_index":{"The":[0],"rising":[1],"ubiquity":[2],"of":[3,19,67,126,202],"Convolutional":[4],"Neural":[5],"Networks":[6],"(CNN)":[7],"for":[8,48,104,137],"learning":[9],"tasks":[10],"has":[11,55],"led":[12,56],"to":[13,57,78,101,122,133,148,154,168],"their":[14],"use":[15,105,169],"on":[16,25,83,106,177,181,184],"a":[17,38,80,95,124,138,159,165,187],"variety":[18],"devices.":[20,108],"CNNs":[21],"can":[22,172,193],"be":[23,52,194],"used":[24],"small":[26,81,107,185],"devices,":[27,44,186],"such":[28],"as":[29,158],"phones":[30],"or":[31],"embedded":[32],"systems;":[33],"however,":[34],"compute":[35],"time":[36],"is":[37,69,99,143,152],"critical":[39],"enabling":[40],"factor.":[41],"On":[42],"these":[43],"trading":[45],"high":[46],"accuracy":[47,176],"improved":[49],"performance":[50],"may":[51],"worthwhile.":[53],"This":[54],"active":[58],"research":[59],"in":[60,72],"high-level":[61],"convolution":[62,97],"optimizations.":[63],"One":[64],"successful":[65],"class":[66],"optimizations":[68],"filter":[70],"pruning,":[71],"which":[73],"filters":[74,129],"that":[75,98,130,164],"are":[76,88,131],"determined":[77],"have":[79],"effect":[82],"the":[84,127,198],"network's":[85],"final":[86],"output":[87],"deleted.":[89],"In":[90],"this":[91],"work,":[92],"we":[93],"present":[94],"self-pruning":[96],"intended":[100],"accelerate":[102],"convolutions":[103],"We":[109],"call":[110],"it":[111,116,145],"an":[112],"ALSH":[113,170],"Convolution":[114],"because":[115],"uses":[117],"Asymmetric":[118],"Locality":[119],"Sensitive":[120],"Hashing":[121],"generate":[123],"subset":[125],"convolution's":[128],"likely":[132],"produce":[134],"large":[135],"outputs":[136],"given":[139],"input.":[140],"Our":[141],"methodology":[142],"accessible:":[144],"generalizes":[146],"well":[147],"many":[149],"architectures":[150],"and":[151,179],"easy":[153],"use,":[155],"essentially":[156],"functioning":[157],"regular":[160],"layer.":[161],"Experiments":[162],"show":[163],"network":[166,188,200],"modified":[167],"Convolutions":[171],"stay":[173],"within":[174],"5%":[175],"CIFAR-10":[178],"10%":[180],"CIFAR-100.":[182],"Further,":[183],"built":[189],"with":[190],"our":[191],"implementation":[192],"2\u00d7":[195],"faster":[196],"than":[197],"same":[199],"composed":[201],"PyTorch's":[203],"convolution.":[204]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
