{"id":"https://openalex.org/W3189043577","doi":"https://doi.org/10.1145/3460972","title":"An Energy-Efficient Inference Method in Convolutional Neural Networks Based on Dynamic Adjustment of the Pruning Level","display_name":"An Energy-Efficient Inference Method in Convolutional Neural Networks Based on Dynamic Adjustment of the Pruning Level","publication_year":2021,"publication_date":"2021-08-01","ids":{"openalex":"https://openalex.org/W3189043577","doi":"https://doi.org/10.1145/3460972","mag":"3189043577"},"language":"en","primary_location":{"id":"doi:10.1145/3460972","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460972","pdf_url":null,"source":{"id":"https://openalex.org/S105046310","display_name":"ACM Transactions on Design Automation of Electronic Systems","issn_l":"1084-4309","issn":["1084-4309","1557-7309"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Design Automation of Electronic Systems","raw_type":"journal-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/A5052702825","display_name":"Mohammad Ali Maleki","orcid":"https://orcid.org/0000-0002-9019-3605"},"institutions":[{"id":"https://openalex.org/I23946033","display_name":"University of Tehran","ror":"https://ror.org/05vf56z40","country_code":"IR","type":"education","lineage":["https://openalex.org/I23946033"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Mohammad-Ali Maleki","raw_affiliation_strings":["University of Tehran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Tehran","institution_ids":["https://openalex.org/I23946033"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040865290","display_name":"Alireza Nabipour-Meybodi","orcid":null},"institutions":[{"id":"https://openalex.org/I23946033","display_name":"University of Tehran","ror":"https://ror.org/05vf56z40","country_code":"IR","type":"education","lineage":["https://openalex.org/I23946033"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Alireza Nabipour-Meybodi","raw_affiliation_strings":["University of Tehran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Tehran","institution_ids":["https://openalex.org/I23946033"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042809860","display_name":"Mehdi Kamal","orcid":"https://orcid.org/0000-0001-7098-6440"},"institutions":[{"id":"https://openalex.org/I23946033","display_name":"University of Tehran","ror":"https://ror.org/05vf56z40","country_code":"IR","type":"education","lineage":["https://openalex.org/I23946033"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Mehdi Kamal","raw_affiliation_strings":["University of Tehran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Tehran","institution_ids":["https://openalex.org/I23946033"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074063358","display_name":"Ali Afzali\u2010Kusha","orcid":"https://orcid.org/0000-0001-8614-2007"},"institutions":[{"id":"https://openalex.org/I23946033","display_name":"University of Tehran","ror":"https://ror.org/05vf56z40","country_code":"IR","type":"education","lineage":["https://openalex.org/I23946033"]},{"id":"https://openalex.org/I4210146419","display_name":"Institute for Research in Fundamental Sciences","ror":"https://ror.org/04xreqs31","country_code":"IR","type":"facility","lineage":["https://openalex.org/I4210146419"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Ali Afzali-Kusha","raw_affiliation_strings":["University of Tehran and Institute for Research in Fundamental Sciences"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Tehran and Institute for Research in Fundamental Sciences","institution_ids":["https://openalex.org/I4210146419","https://openalex.org/I23946033"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044650311","display_name":"Massoud Pedram","orcid":"https://orcid.org/0000-0002-2677-7307"},"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"]},{"id":"https://openalex.org/I2800817003","display_name":"California Southern University","ror":"https://ror.org/058zz0t50","country_code":"US","type":"education","lineage":["https://openalex.org/I2800817003"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Massoud Pedram","raw_affiliation_strings":["University of Southern California"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southern California","institution_ids":["https://openalex.org/I2800817003","https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2911,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.55613619,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"26","issue":"6","first_page":"1","last_page":"20"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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/T10036","display_name":"Advanced Neural Network Applications","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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9976999759674072,"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"}},{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8881566524505615},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.8571778535842896},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7913961410522461},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7197605967521667},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.6283871531486511},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5512580871582031},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5089545249938965},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.4975323975086212},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4971313774585724},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.49208372831344604},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4464850425720215},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4428962767124176},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.43730318546295166},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34117981791496277},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07257574796676636},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.05924379825592041}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8881566524505615},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.8571778535842896},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7913961410522461},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7197605967521667},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6283871531486511},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5512580871582031},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5089545249938965},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4975323975086212},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4971313774585724},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.49208372831344604},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4464850425720215},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4428962767124176},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.43730318546295166},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34117981791496277},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07257574796676636},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.05924379825592041},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3460972","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3460972","pdf_url":null,"source":{"id":"https://openalex.org/S105046310","display_name":"ACM Transactions on Design Automation of Electronic Systems","issn_l":"1084-4309","issn":["1084-4309","1557-7309"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Design Automation of Electronic Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1845051632","https://openalex.org/W1984541135","https://openalex.org/W1998917233","https://openalex.org/W2008967810","https://openalex.org/W2119112357","https://openalex.org/W2119144962","https://openalex.org/W2133796295","https://openalex.org/W2289252105","https://openalex.org/W2300242332","https://openalex.org/W2404672516","https://openalex.org/W2469490737","https://openalex.org/W2515385951","https://openalex.org/W2553910756","https://openalex.org/W2554302513","https://openalex.org/W2565305208","https://openalex.org/W2586565528","https://openalex.org/W2593245696","https://openalex.org/W2604319603","https://openalex.org/W2606722458","https://openalex.org/W2625222559","https://openalex.org/W2748528844","https://openalex.org/W2754396617","https://openalex.org/W2905741102","https://openalex.org/W2953212265","https://openalex.org/W3001665736","https://openalex.org/W7074210132"],"related_works":["https://openalex.org/W4293226380","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/W1993210935","https://openalex.org/W4205470293"],"abstract_inverted_index":{"In":[0,64],"this":[1,187],"article,":[2],"we":[3],"present":[4],"a":[5,25,36,67,124],"low-energy":[6],"inference":[7,43,167],"method":[8,44,121,135,150,157,168,188],"for":[9,76,88,123,176],"convolutional":[10,95],"neural":[11,50],"networks":[12,51],"in":[13],"image":[14],"classification":[15],"applications.":[16],"The":[17,85,148,162],"lower":[18],"energy":[19,130,194],"consumption":[20],"is":[21,113,136,151,169],"achieved":[22],"by":[23,118],"using":[24],"highly":[26],"pruned":[27,49,57,83,102],"(lower-energy)":[28],"network":[29,33,160],"if":[30],"the":[31,41,65,73,77,81,93,100,106,109,119,129,133,140,155,159,165,179,198,202,210],"resulting":[32],"can":[34],"provide":[35],"correct":[37],"output.":[38],"More":[39],"specifically,":[40],"proposed":[42,120,134,149,166],"makes":[45,70],"use":[46,71],"of":[47,72,80,99,108,127,132,142,153,164,178,204],"two":[48],"(NNs),":[52],"namely":[53],"mildly":[54],"and":[55,158],"aggressively":[56,101],"networks,":[58],"which":[59],"are":[60],"both":[61,154],"designed":[62],"offline.":[63],"system,":[66],"third":[68,86],"NN":[69,181,200],"input":[74],"data":[75],"online":[78,110],"selection":[79],"appropriate":[82],"network.":[84],"network,":[87],"its":[89],"feature":[90],"extraction,":[91],"employs":[92],"same":[94],"layers":[96],"as":[97],"those":[98],"NN,":[103],"thereby":[104],"reducing":[105],"overhead":[107],"management.":[111],"There":[112],"some":[114,177],"accuracy":[115,207],"loss":[116,208],"induced":[117],"where,":[122],"given":[125],"level":[126],"accuracy,":[128],"gain":[131],"considerably":[137],"larger":[138],"than":[139],"case":[141],"employing":[143],"any":[144],"one":[145],"pruning":[146,156],"level.":[147],"independent":[152],"architecture.":[161],"efficacy":[163],"assessed":[170],"on":[171,191,209],"Eyeriss":[172],"hardware":[173],"accelerator":[174],"platform":[175],"state-of-the-art":[180],"architectures.":[182],"Our":[183],"studies":[184],"show":[185],"that":[186],"may":[189],"provide,":[190],"average,":[192],"70%":[193],"reduction":[195],"compared":[196],"to":[197],"original":[199],"at":[201],"cost":[203],"about":[205],"3%":[206],"CIFAR-10":[211],"dataset.":[212]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
