{"id":"https://openalex.org/W3021285772","doi":"https://doi.org/10.1109/nanoarch47378.2019.181286","title":"Detecting and Bypassing Trivial Computations in Convolutional Neural Networks","display_name":"Detecting and Bypassing Trivial Computations in Convolutional Neural Networks","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W3021285772","doi":"https://doi.org/10.1109/nanoarch47378.2019.181286","mag":"3021285772"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/nanoarch47378.2019.181286","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5033356396","display_name":"Dongning Ma","orcid":"https://orcid.org/0000-0002-1879-4406"},"institutions":[{"id":"https://openalex.org/I7863295","display_name":"Villanova University","ror":"https://ror.org/02g7kd627","country_code":"US","type":"education","lineage":["https://openalex.org/I7863295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dongning Ma","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Villanova University"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Villanova University","institution_ids":["https://openalex.org/I7863295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016146461","display_name":"Xun Jiao","orcid":"https://orcid.org/0000-0003-4476-2501"},"institutions":[{"id":"https://openalex.org/I7863295","display_name":"Villanova University","ror":"https://ror.org/02g7kd627","country_code":"US","type":"education","lineage":["https://openalex.org/I7863295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xun Jiao","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Villanova University"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Villanova University","institution_ids":["https://openalex.org/I7863295"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.064,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.224675,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":61,"max":69},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9999,"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":0.9999,"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":0.9975,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9972,"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/mnist-database","display_name":"MNIST database","score":0.776698},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.745965}],"concepts":[{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.8162694},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.776698},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7475154},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.745965},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7210543},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.65277356},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.44347498},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4181244},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.4097295},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3991491},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36945194},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","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}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/nanoarch47378.2019.181286","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.91,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":26,"referenced_works":["https://openalex.org/W1998917233","https://openalex.org/W2090557012","https://openalex.org/W2112796928","https://openalex.org/W2119144962","https://openalex.org/W2160815625","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2285660444","https://openalex.org/W2286365479","https://openalex.org/W2395566064","https://openalex.org/W2438799339","https://openalex.org/W2442974303","https://openalex.org/W2516141709","https://openalex.org/W2533121491","https://openalex.org/W2549139847","https://openalex.org/W2581082771","https://openalex.org/W2590796488","https://openalex.org/W2613103714","https://openalex.org/W2613119772","https://openalex.org/W2750173518","https://openalex.org/W2799131456","https://openalex.org/W2906043559","https://openalex.org/W2950656546","https://openalex.org/W2963367920","https://openalex.org/W2964299589","https://openalex.org/W4299322810"],"related_works":["https://openalex.org/W992687842","https://openalex.org/W4309224979","https://openalex.org/W4300560302","https://openalex.org/W3214410901","https://openalex.org/W3204400881","https://openalex.org/W3204296682","https://openalex.org/W3183118997","https://openalex.org/W3088108839","https://openalex.org/W3036048022","https://openalex.org/W3026879719"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1],"networks":[2],"(CNNs)":[3],"recently":[4],"are":[5],"able":[6],"to":[7,51,66,120,152,179],"exceed":[8],"human":[9],"accuracy":[10,158],"in":[11],"various":[12],"application":[13],"domains":[14],"such":[15,41,169,180],"as":[16,42,86,170],"image":[17],"recognition,":[18],"medical":[19],"diagnosis,":[20],"and":[21,44,58,77,106,141,172],"financial":[22],"analysis.":[23],"However,":[24],"the":[25,68,79,87,89,122,127,130,145],"high":[26,32],"computational":[27],"complexity":[28],"of":[29,71,91,112],"CNNs":[30,72,146],"incurs":[31],"energy":[33,154],"consumption":[34],"on":[35,139],"current":[36],"hardware":[37],"implementations.":[38],"Existing":[39],"solutions":[40],"pruning":[43,171],"quantization":[45,173],"typically":[46],"require":[47],"retraining":[48],"or":[49,110],"fine-tuning":[50],"regain":[52],"accuracy,":[53],"which":[54,92],"can":[55,93,150,162],"be":[56,94,163],"cost-prohibitive":[57],"time-consuming.":[59],"This":[60,160],"paper":[61],"proposes":[62],"a":[63],"retraining-free":[64],"approach":[65],"reducing":[67],"computation":[69],"workload":[70],"during":[73],"inference":[74],"by":[75],"detecting":[76],"bypassing":[78],"trivial":[80,84,123],"computations.":[81,98,124],"We":[82],"define":[83],"computations":[85,88],"results":[90,138],"determined":[95],"without":[96,133,156],"actual":[97,135],"The":[99],"examples":[100],"include":[101],"multiplication":[102],"with":[103,108,147,166],"0,":[104],"+1/-1":[105],"addition":[107,111],"0":[109],"opposite":[113],"numbers.":[114],"Correspondingly,":[115],"we":[116],"develop":[117],"bypass":[118,148],"circuits":[119,149],"detect":[121],"Once":[125],"detected,":[126],"circuit":[128],"delivers":[129],"predetermined":[131],"result":[132],"an":[134],"computation.":[136],"Experimental":[137],"MNIST":[140],"EMNIST":[142],"show":[143],"that":[144],"lead":[151],"30.66-33.52%":[153],"savings":[155],"any":[157],"loss.":[159],"technique":[161],"used":[164],"together":[165],"existing":[167],"techniques":[168],"because":[174],"it":[175],"is":[176],"totally":[177],"complimentary":[178],"techniques.":[181]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3021285772","counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-01-06T10:54:56.844574","created_date":"2020-05-13"}