{"id":"https://openalex.org/W3091344897","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207281","title":"Effective Post-Training Quantization Of Neural Networks For Inference on Low Power Neural Accelerator","display_name":"Effective Post-Training Quantization Of Neural Networks For Inference on Low Power Neural Accelerator","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3091344897","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207281","mag":"3091344897"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207281","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207281","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5112434840","display_name":"Alexander Demidovskij","orcid":null},"institutions":[{"id":"https://openalex.org/I118501908","display_name":"National Research University Higher School of Economics","ror":"https://ror.org/055f7t516","country_code":"RU","type":"education","lineage":["https://openalex.org/I118501908"]}],"countries":["RU"],"is_corresponding":true,"raw_author_name":"Alexander Demidovskij","raw_affiliation_strings":["Computer Science Department, Higher School of Economics Intel Corporation, Nizhny Novgorod, Russia"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, Higher School of Economics Intel Corporation, Nizhny Novgorod, Russia","institution_ids":["https://openalex.org/I118501908"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100303626","display_name":"Eugene Smirnov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eugene Smirnov","raw_affiliation_strings":["Intel Corporation, Nizhny Novgorod, Russia"],"affiliations":[{"raw_affiliation_string":"Intel Corporation, Nizhny Novgorod, Russia","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5112434840"],"corresponding_institution_ids":["https://openalex.org/I118501908"],"apc_list":null,"apc_paid":null,"fwci":0.9279,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.8065991,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9973000288009644,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9973000288009644,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.996999979019165,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9945999979972839,"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/quantization","display_name":"Quantization (signal processing)","score":0.8367131948471069},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7311078906059265},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.6619196534156799},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6520745754241943},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6376352310180664},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5323396325111389},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.5025599002838135},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.4757097363471985},{"id":"https://openalex.org/keywords/coprocessor","display_name":"Coprocessor","score":0.465238481760025},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.43064114451408386},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40620070695877075},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.29684317111968994},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2653627395629883}],"concepts":[{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.8367131948471069},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7311078906059265},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.6619196534156799},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6520745754241943},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6376352310180664},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5323396325111389},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.5025599002838135},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.4757097363471985},{"id":"https://openalex.org/C86111242","wikidata":"https://www.wikidata.org/wiki/Q859595","display_name":"Coprocessor","level":2,"score":0.465238481760025},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.43064114451408386},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40620070695877075},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.29684317111968994},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2653627395629883},{"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/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207281","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207281","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1841592590","https://openalex.org/W1902934009","https://openalex.org/W2009692332","https://openalex.org/W2286365479","https://openalex.org/W2524428287","https://openalex.org/W2582794520","https://openalex.org/W2593245696","https://openalex.org/W2785540404","https://openalex.org/W2791111022","https://openalex.org/W2791845808","https://openalex.org/W2799002811","https://openalex.org/W2809624076","https://openalex.org/W2963114950","https://openalex.org/W2963374099","https://openalex.org/W2963674932","https://openalex.org/W2963935227","https://openalex.org/W2964228333","https://openalex.org/W2971820800","https://openalex.org/W3006349509","https://openalex.org/W3101616732","https://openalex.org/W4245873651","https://openalex.org/W6638783484","https://openalex.org/W6639703010","https://openalex.org/W6696004547","https://openalex.org/W6727208969","https://openalex.org/W6732866452","https://openalex.org/W6734062232","https://openalex.org/W6747599758","https://openalex.org/W6753069482","https://openalex.org/W6767404107","https://openalex.org/W6774050237"],"related_works":["https://openalex.org/W4386245174","https://openalex.org/W4200132709","https://openalex.org/W3204400881","https://openalex.org/W3214410901","https://openalex.org/W3204296682","https://openalex.org/W3183118997","https://openalex.org/W2917767146","https://openalex.org/W3176282186","https://openalex.org/W4387489555","https://openalex.org/W2973622361"],"abstract_inverted_index":{"Neural":[0],"network":[1],"deployment":[2,51],"to":[3,48,57,83],"the":[4,45,50,58],"target":[5],"environment":[6],"is":[7,69,100],"considered":[8],"a":[9,74,103,126],"challenging":[10],"task":[11],"especially":[12],"because":[13],"of":[14,17,32,79,105],"heavy":[15],"burden":[16],"hardware":[18],"requirements":[19],"that":[20,66,120],"DNN":[21],"models":[22],"lay":[23],"on":[24,102,130],"computation":[25],"capabilities":[26],"and":[27,64,123,136],"power":[28,34],"consumption.":[29],"In":[30],"case":[31],"low":[33],"edge":[35],"devices,":[36],"such":[37],"as":[38,116,125],"GNA":[39,124],"-":[40],"neural":[41],"coprocessor,":[42],"quantization":[43,60,76,122],"becomes":[44],"only":[46],"way":[47],"make":[49],"possible.":[52],"This":[53],"paper":[54],"draws":[55],"attention":[56],"post-training":[59],"for":[61],"low-power":[62],"devices":[63],"proves":[65],"this":[67],"approach":[68,99],"practically":[70],"effective.":[71],"We":[72],"propose":[73],"novel":[75],"algorithm":[77],"capable":[78],"reducing":[80],"DNNs":[81],"precision":[82],"16-bit":[84],"or":[85],"8-bit":[86],"integer":[87],"with":[88,113],"negligible":[89],"drop":[90],"in":[91,110],"accuracy":[92],"(less":[93],"than":[94],"0.1":[95],"percent).":[96],"The":[97],"elaborated":[98],"demonstrated":[101],"set":[104],"speech":[106],"recognition":[107],"networks":[108],"trained":[109],"Kaldi":[111],"framework":[112,115],"OpenVINO":[114],"an":[117],"inference":[118],"backend":[119],"supports":[121],"target.":[127],"Quantization":[128],"influence":[129],"original":[131],"topologies":[132],"was":[133],"rigorously":[134],"measured":[135],"analyzed.":[137]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
