{"id":"https://openalex.org/W2891442728","doi":"https://doi.org/10.1109/jssc.2018.2867275","title":"A Variation-Tolerant In-Memory Machine Learning Classifier via On-Chip Training","display_name":"A Variation-Tolerant In-Memory Machine Learning Classifier via On-Chip Training","publication_year":2018,"publication_date":"2018-09-12","ids":{"openalex":"https://openalex.org/W2891442728","doi":"https://doi.org/10.1109/jssc.2018.2867275","mag":"2891442728"},"language":"en","primary_location":{"id":"doi:10.1109/jssc.2018.2867275","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jssc.2018.2867275","pdf_url":null,"source":{"id":"https://openalex.org/S83637746","display_name":"IEEE Journal of Solid-State Circuits","issn_l":"0018-9200","issn":["0018-9200","1558-173X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Solid-State Circuits","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/A5030673462","display_name":"Sujan K. Gonugondla","orcid":"https://orcid.org/0000-0003-4743-6461"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sujan K. Gonugondla","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Illinois at Urbana\u2013Champaign, Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Illinois at Urbana\u2013Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006712350","display_name":"Mingu Kang","orcid":"https://orcid.org/0000-0001-8104-5136"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingu Kang","raw_affiliation_strings":["IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057014407","display_name":"Naresh R. Shanbhag","orcid":"https://orcid.org/0000-0002-4323-9164"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Naresh R. Shanbhag","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Illinois at Urbana\u2013Champaign, Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Illinois at Urbana\u2013Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5030673462"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":7.8539,"has_fulltext":false,"cited_by_count":119,"citation_normalized_percentile":{"value":0.97906221,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"53","issue":"11","first_page":"3163","last_page":"3173"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9998000264167786,"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/T12676","display_name":"Machine Learning and ELM","score":0.9983000159263611,"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/computer-science","display_name":"Computer science","score":0.6200841069221497},{"id":"https://openalex.org/keywords/cmos","display_name":"CMOS","score":0.6126741170883179},{"id":"https://openalex.org/keywords/static-random-access-memory","display_name":"Static random-access memory","score":0.5598821043968201},{"id":"https://openalex.org/keywords/chip","display_name":"Chip","score":0.5420722365379333},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.5404655933380127},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.4355012774467468},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43388402462005615},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.3451343774795532},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.27117329835891724},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.20466399192810059},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18900102376937866},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.18780699372291565}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6200841069221497},{"id":"https://openalex.org/C46362747","wikidata":"https://www.wikidata.org/wiki/Q173431","display_name":"CMOS","level":2,"score":0.6126741170883179},{"id":"https://openalex.org/C68043766","wikidata":"https://www.wikidata.org/wiki/Q267416","display_name":"Static random-access memory","level":2,"score":0.5598821043968201},{"id":"https://openalex.org/C165005293","wikidata":"https://www.wikidata.org/wiki/Q1074500","display_name":"Chip","level":2,"score":0.5420722365379333},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.5404655933380127},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.4355012774467468},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43388402462005615},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.3451343774795532},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.27117329835891724},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.20466399192810059},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18900102376937866},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.18780699372291565},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jssc.2018.2867275","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jssc.2018.2867275","pdf_url":null,"source":{"id":"https://openalex.org/S83637746","display_name":"IEEE Journal of Solid-State Circuits","issn_l":"0018-9200","issn":["0018-9200","1558-173X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Solid-State Circuits","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.9100000262260437}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1601138654","https://openalex.org/W1999085092","https://openalex.org/W2119821739","https://openalex.org/W2146276996","https://openalex.org/W2152839228","https://openalex.org/W2267635276","https://openalex.org/W2289252105","https://openalex.org/W2436219157","https://openalex.org/W2591601611","https://openalex.org/W2592389822","https://openalex.org/W2593564159","https://openalex.org/W2594492285","https://openalex.org/W2705659776","https://openalex.org/W2741318576","https://openalex.org/W2747730547","https://openalex.org/W2769224948","https://openalex.org/W2782511028","https://openalex.org/W2790511620","https://openalex.org/W2790556218","https://openalex.org/W2791598392","https://openalex.org/W2792893539","https://openalex.org/W2794141774","https://openalex.org/W2794288888","https://openalex.org/W2799821226","https://openalex.org/W2963029056","https://openalex.org/W2963433607","https://openalex.org/W4212788319","https://openalex.org/W4236432903","https://openalex.org/W4239510810","https://openalex.org/W4295262505","https://openalex.org/W6693397755","https://openalex.org/W6734592959","https://openalex.org/W6742116046","https://openalex.org/W6748877789","https://openalex.org/W6748967906","https://openalex.org/W6750171416"],"related_works":["https://openalex.org/W3151633427","https://openalex.org/W2012045996","https://openalex.org/W3024050170","https://openalex.org/W2109451123","https://openalex.org/W4293253840","https://openalex.org/W4378977321","https://openalex.org/W2967161359","https://openalex.org/W4308090481","https://openalex.org/W3211992815","https://openalex.org/W2119025037"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,11,19,143,183,199],"robust":[4,135],"deep":[5,26],"in-memory":[6,27],"machine":[7],"learning":[8,129],"classifier":[9],"with":[10,198],"stochastic":[12],"gradient":[13],"descent":[14],"(SGD)-based":[15],"on-chip":[16,123,132],"trainer":[17],"using":[18],"standard":[20],"16-kB":[21],"6T":[22],"SRAM":[23],"array.":[24,62],"The":[25,112,154,203],"architecture":[28],"(DIMA)":[29],"enhances":[30],"both":[31],"energy":[32,68,110,148,189,204],"efficiency":[33,69],"and":[34,51,70,81,190,218],"throughput":[35],"over":[36,149],"conventional":[37,200],"digital":[38,201],"architectures":[39],"by":[40,52,120],"reading":[41],"multiple":[42],"bits":[43],"per":[44,48,210],"bit":[45],"line":[46],"(BL)":[47],"read":[49],"cycle":[50],"employing":[53],"mixed-signal":[54],"processing":[55],"in":[56,99,147,157,188,193],"the":[57,60,67],"periphery":[58],"of":[59,185,206,216],"bit-cell":[61],"Though":[63],"these":[64],"techniques":[65],"improve":[66],"latency,":[71],"DIMA's":[72],"analog":[73],"nature":[74],"makes":[75],"it":[76],"sensitive":[77],"to":[78,94,96,142,168],"process,":[79],"voltage,":[80],"temperature":[82],"(PVT)":[83],"variations,":[84],"especially":[85],"under":[86,137],"reduced":[87,138],"BL":[88,139],"swings.":[89],"On-chip":[90],"training":[91,133,207],"enables":[92,134],"DIMA":[93],"adapt":[95],"chip-specific":[97],"variations":[98],"PVT":[100],"as":[101,103,196],"well":[102],"data":[104],"statistics,":[105],"thereby":[106,181],"further":[107],"enhancing":[108],"its":[109],"efficiency.":[111],"65-nm":[113,158],"CMOS":[114,159],"prototype":[115,155],"IC":[116,156],"demonstrates":[117],"this":[118],"improvement":[119],"realizing":[121],"an":[122,150],"trainable":[124],"support":[125],"vector":[126],"machine.":[127],"By":[128],"chipspecific":[130],"weights,":[131],"operation":[136],"swing":[140],"leading":[141],"2.4":[144],"times":[145,187,192],"reduction":[146,184],"off-chip":[151],"trained":[152],"DIMA.":[153],"consumes":[160],"42":[161],"pJ/decision":[162],"at":[163],"32":[164],"M":[165],"decisions/s,":[166],"corresponding":[167],"3.12":[169],"TOPS/W":[170],"(1":[171],"OP":[172],"=":[173],"one":[174],"8-b":[175,177],"\u00d7":[176],"MAC)":[178],"during":[179],"inference,":[180],"achieving":[182],"21":[186],"100":[191],"energy-delay":[194],"product":[195],"compared":[197],"architecture.":[202],"overhead":[205],"is":[208],"<;26%":[209],"decision":[211],"for":[212],"SGD":[213],"batch":[214],"sizes":[215],"128":[217],"higher.":[219]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":18},{"year":2021,"cited_by_count":22},{"year":2020,"cited_by_count":24},{"year":2019,"cited_by_count":14},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
