{"id":"https://openalex.org/W3091041533","doi":"https://doi.org/10.1109/iscas45731.2020.9181236","title":"Full Approximation of Deep Neural Networks through Efficient Optimization","display_name":"Full Approximation of Deep Neural Networks through Efficient Optimization","publication_year":2020,"publication_date":"2020-09-29","ids":{"openalex":"https://openalex.org/W3091041533","doi":"https://doi.org/10.1109/iscas45731.2020.9181236","mag":"3091041533"},"language":"en","primary_location":{"id":"doi:10.1109/iscas45731.2020.9181236","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas45731.2020.9181236","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Symposium on Circuits and Systems (ISCAS)","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/A5069309172","display_name":"Cecilia De la Parra","orcid":"https://orcid.org/0000-0002-1463-6822"},"institutions":[{"id":"https://openalex.org/I889804353","display_name":"Robert Bosch (Germany)","ror":"https://ror.org/01fe0jt45","country_code":"DE","type":"company","lineage":["https://openalex.org/I889804353"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Cecilia De la Parra","raw_affiliation_strings":["Robert Bosch GmbH, Renningen, Germany"],"affiliations":[{"raw_affiliation_string":"Robert Bosch GmbH, Renningen, Germany","institution_ids":["https://openalex.org/I889804353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056912495","display_name":"Andre Guntoro","orcid":"https://orcid.org/0000-0003-4144-0283"},"institutions":[{"id":"https://openalex.org/I889804353","display_name":"Robert Bosch (Germany)","ror":"https://ror.org/01fe0jt45","country_code":"DE","type":"company","lineage":["https://openalex.org/I889804353"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andre Guntoro","raw_affiliation_strings":["Robert Bosch GmbH, Renningen, Germany"],"affiliations":[{"raw_affiliation_string":"Robert Bosch GmbH, Renningen, Germany","institution_ids":["https://openalex.org/I889804353"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100755285","display_name":"Akash Kumar","orcid":"https://orcid.org/0000-0001-7125-1737"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Akash Kumar","raw_affiliation_strings":["Technological University of Dresden, Dresden, Germany"],"affiliations":[{"raw_affiliation_string":"Technological University of Dresden, Dresden, Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5069309172"],"corresponding_institution_ids":["https://openalex.org/I889804353"],"apc_list":null,"apc_paid":null,"fwci":0.2055,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.51521183,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10363","display_name":"Low-power high-performance VLSI design","score":0.9988999962806702,"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"}},"topics":[{"id":"https://openalex.org/T10363","display_name":"Low-power high-performance VLSI design","score":0.9988999962806702,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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":0.9973999857902527,"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":0.7164889574050903},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5988435745239258},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5957500338554382},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.590092658996582},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5513871312141418},{"id":"https://openalex.org/keywords/multiplier","display_name":"Multiplier (economics)","score":0.5386016964912415},{"id":"https://openalex.org/keywords/approximation-error","display_name":"Approximation error","score":0.533944308757782},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.5272483229637146},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.4409436285495758},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.439419150352478},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.43018850684165955},{"id":"https://openalex.org/keywords/approximation-algorithm","display_name":"Approximation algorithm","score":0.41771239042282104},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.34296685457229614},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3162183165550232},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15584325790405273},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07669264078140259}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7164889574050903},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5988435745239258},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5957500338554382},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.590092658996582},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5513871312141418},{"id":"https://openalex.org/C124584101","wikidata":"https://www.wikidata.org/wiki/Q1053266","display_name":"Multiplier (economics)","level":2,"score":0.5386016964912415},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.533944308757782},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.5272483229637146},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.4409436285495758},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.439419150352478},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.43018850684165955},{"id":"https://openalex.org/C148764684","wikidata":"https://www.wikidata.org/wiki/Q621751","display_name":"Approximation algorithm","level":2,"score":0.41771239042282104},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.34296685457229614},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3162183165550232},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15584325790405273},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07669264078140259},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscas45731.2020.9181236","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas45731.2020.9181236","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8999999761581421,"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/W1841592590","https://openalex.org/W1974078116","https://openalex.org/W1998917233","https://openalex.org/W2028499920","https://openalex.org/W2155893237","https://openalex.org/W2165432382","https://openalex.org/W2194775991","https://openalex.org/W2533121491","https://openalex.org/W2612139336","https://openalex.org/W2748818695","https://openalex.org/W2804268686","https://openalex.org/W2871705258","https://openalex.org/W2909711603","https://openalex.org/W2921077359","https://openalex.org/W2938536078","https://openalex.org/W2958306322","https://openalex.org/W2962719505","https://openalex.org/W2963374099","https://openalex.org/W2964299589","https://openalex.org/W3036274717","https://openalex.org/W3102684668","https://openalex.org/W3103768881","https://openalex.org/W3138798301","https://openalex.org/W4236363946","https://openalex.org/W6638783484","https://openalex.org/W6760395591"],"related_works":["https://openalex.org/W2110817230","https://openalex.org/W3091355678","https://openalex.org/W1606488336","https://openalex.org/W1988569331","https://openalex.org/W4255193494","https://openalex.org/W189995875","https://openalex.org/W2284958355","https://openalex.org/W2962894463","https://openalex.org/W2950077880","https://openalex.org/W4301487118"],"abstract_inverted_index":{"Approximate":[0],"Computing":[1],"is":[2],"a":[3,47],"promising":[4],"paradigm":[5],"for":[6,49,55],"mitigating":[7],"computational":[8],"requirements":[9],"of":[10,18,26,40,60,67,95,105,113],"Deep":[11],"Neural":[12],"Networks":[13],"(DNN),":[14],"by":[15],"taking":[16],"advantage":[17],"their":[19],"inherent":[20],"error":[21],"resilience.":[22],"Specifically,":[23],"the":[24,68,80,100],"use":[25],"approximate":[27,51,77],"multipliers":[28,78],"in":[29,37],"DNN":[30,42],"inference":[31],"can":[32],"lead":[33],"to":[34,97,107],"significant":[35],"improvements":[36],"power":[38],"consumption":[39],"embedded":[41],"applications.":[43],"This":[44],"paper":[45],"presents":[46],"methodology":[48,74],"efficient":[50],"multiplier":[52],"selection":[53],"and":[54,57,65,91,104],"full":[56],"uniform":[58],"approximation":[59,69],"large":[61],"DNNs,":[62],"through":[63],"retraining":[64],"minimization":[66],"error.":[70],"We":[71],"evaluate":[72],"our":[73],"using":[75],"422":[76],"from":[79],"EvoApprox":[81],"library,":[82],"with":[83,89,109],"three":[84],"different":[85],"Residual":[86],"architectures":[87],"trained":[88],"Cifar10,":[90],"achieve":[92],"energy":[93],"savings":[94],"up":[96,106],"18%":[98],"surpassing":[99],"original":[101],"floating-point":[102],"accuracy,":[103],"58%":[108],"an":[110],"accuracy":[111],"loss":[112],"0.73%.":[114]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
