{"id":"https://openalex.org/W2799026378","doi":"https://doi.org/10.23919/date.2018.8342119","title":"Exploiting approximate computing for deep learning acceleration","display_name":"Exploiting approximate computing for deep learning acceleration","publication_year":2018,"publication_date":"2018-03-01","ids":{"openalex":"https://openalex.org/W2799026378","doi":"https://doi.org/10.23919/date.2018.8342119","mag":"2799026378"},"language":"en","primary_location":{"id":"doi:10.23919/date.2018.8342119","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date.2018.8342119","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Design, Automation & Test in Europe Conference & Exhibition (DATE)","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/A5101406569","display_name":"Chia\u2010Yu Chen","orcid":"https://orcid.org/0000-0001-5542-7149"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chia-Yu Chen","raw_affiliation_strings":["IBM T. J. Watson Research Center"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078440061","display_name":"Jungwook Choi","orcid":"https://orcid.org/0000-0002-3075-8694"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jungwook Choi","raw_affiliation_strings":["IBM T. J. Watson Research Center"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109912930","display_name":"Kailash Gopalakrishnan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kailash Gopalakrishnan","raw_affiliation_strings":["IBM T. J. Watson Research Center"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101784873","display_name":"V. Srinivasan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Viji Srinivasan","raw_affiliation_strings":["IBM T. J. Watson Research Center"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010094713","display_name":"Swagath Venkataramani","orcid":"https://orcid.org/0000-0002-0470-6364"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Swagath Venkataramani","raw_affiliation_strings":["IBM T. J. Watson Research Center"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101406569"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.1942,"has_fulltext":false,"cited_by_count":62,"citation_normalized_percentile":{"value":0.97078321,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"821","last_page":"826"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9987000226974487,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9987000226974487,"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/T11321","display_name":"Error Correcting Code Techniques","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8300809264183044},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6625540256500244},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6109604239463806},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5949792265892029},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5073797106742859},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.47885653376579285},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4747065603733063},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4229833483695984},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.41296935081481934},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.41130968928337097},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.35305410623550415},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.147050142288208}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8300809264183044},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6625540256500244},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6109604239463806},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5949792265892029},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5073797106742859},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.47885653376579285},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4747065603733063},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4229833483695984},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.41296935081481934},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.41130968928337097},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.35305410623550415},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.147050142288208},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/date.2018.8342119","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date.2018.8342119","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Design, Automation & Test in Europe Conference & Exhibition (DATE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5699999928474426}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W778657980","https://openalex.org/W1442374986","https://openalex.org/W1665214252","https://openalex.org/W1825672851","https://openalex.org/W1841592590","https://openalex.org/W1902934009","https://openalex.org/W2013305145","https://openalex.org/W2117539524","https://openalex.org/W2132737349","https://openalex.org/W2168231600","https://openalex.org/W2194775991","https://openalex.org/W2242818861","https://openalex.org/W2405578611","https://openalex.org/W2405920868","https://openalex.org/W2407022425","https://openalex.org/W2469490737","https://openalex.org/W2519224033","https://openalex.org/W2524428287","https://openalex.org/W2525778437","https://openalex.org/W2553417306","https://openalex.org/W2560017826","https://openalex.org/W2563343794","https://openalex.org/W2586654419","https://openalex.org/W2594653239","https://openalex.org/W2748818695","https://openalex.org/W2767203761","https://openalex.org/W2786771851","https://openalex.org/W2963114950","https://openalex.org/W2963374099","https://openalex.org/W2963424132","https://openalex.org/W2963903325","https://openalex.org/W2963964896","https://openalex.org/W4230874317","https://openalex.org/W4294371482","https://openalex.org/W6622473587","https://openalex.org/W6628377381","https://openalex.org/W6637242042","https://openalex.org/W6638783484","https://openalex.org/W6679393576","https://openalex.org/W6684859321","https://openalex.org/W6687483927","https://openalex.org/W6713835734","https://openalex.org/W6714239094","https://openalex.org/W6730047919"],"related_works":["https://openalex.org/W4380075502","https://openalex.org/W4223943233","https://openalex.org/W4312200629","https://openalex.org/W4360585206","https://openalex.org/W4364306694","https://openalex.org/W4380086463","https://openalex.org/W4225161397","https://openalex.org/W3014300295","https://openalex.org/W3164822677","https://openalex.org/W2792987183"],"abstract_inverted_index":{"Deep":[0],"Neural":[1],"Networks":[2],"(DNNs)":[3],"have":[4,39],"emerged":[5],"as":[6,25,43],"a":[7],"powerful":[8],"and":[9,33,36,69,95,145,172],"versatile":[10],"set":[11],"of":[12,61,63,67,93,134,164],"techniques":[13,129],"to":[14,121,130],"address":[15],"challenging":[16],"artificial":[17],"intelligence":[18],"(AI)":[19],"problems.":[20],"Applications":[21],"in":[22,77,167],"domains":[23],"such":[24],"image/video":[26],"processing,":[27,30],"natural":[28],"language":[29],"speech":[31],"synthesis":[32],"recognition,":[34],"genomics":[35],"many":[37],"others":[38],"embraced":[40],"deep":[41,136,149],"learning":[42,137,150],"the":[44,91],"foundational":[45],"technique.":[46],"DNNs":[47,82,118],"achieve":[48],"superior":[49],"accuracy":[50],"for":[51,72,148,170,175],"these":[52,103],"applications":[53],"using":[54],"very":[55],"large":[56,84],"models":[57],"which":[58],"require":[59],"100s":[60],"MBs":[62],"data":[64,73],"storage,":[65],"ExaOps":[66],"computation":[68,146],"high":[70],"bandwidth":[71],"movement.":[74],"Despite":[75],"advances":[76],"computing":[78],"systems,":[79],"training":[80,138,171],"state-of-the-art":[81],"on":[83,112],"datasets":[85],"takes":[86],"several":[87],"days/weeks,":[88],"directly":[89],"limiting":[90],"pace":[92],"innovation":[94],"adoption.":[96],"In":[97],"this":[98],"paper,":[99],"we":[100,127],"discuss":[101],"how":[102],"challenges":[104],"can":[105],"be":[106],"addressed":[107],"via":[108,139,152],"approximate":[109,125],"computing.":[110],"Based":[111],"our":[113],"earlier":[114],"studies":[115],"demonstrating":[116],"that":[117],"are":[119],"resilient":[120],"numerical":[122],"errors":[123],"from":[124],"computing,":[126],"present":[128],"reduce":[131],"communication":[132,168],"overhead":[133,169],"distributed":[135],"adaptive":[140],"residual":[141],"gradient":[142],"compression":[143],"(AdaComp),":[144],"cost":[147,174],"inference":[151,176],"Prameterized":[153],"clipping":[154],"ACTivation":[155],"(PACT)":[156],"based":[157],"network":[158],"quantization.":[159],"Experimental":[160],"evaluation":[161],"demonstrates":[162],"order":[163],"magnitude":[165],"savings":[166],"computational":[173],"while":[177],"not":[178],"compromising":[179],"application":[180],"accuracy.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-03-01T08:55:55.761014","created_date":"2025-10-10T00:00:00"}