{"id":"https://openalex.org/W4320008771","doi":"https://doi.org/10.1109/jetcas.2023.3238295","title":"Device Modeling Bias in ReRAM-Based Neural Network Simulations","display_name":"Device Modeling Bias in ReRAM-Based Neural Network Simulations","publication_year":2023,"publication_date":"2023-01-20","ids":{"openalex":"https://openalex.org/W4320008771","doi":"https://doi.org/10.1109/jetcas.2023.3238295"},"language":"en","primary_location":{"id":"doi:10.1109/jetcas.2023.3238295","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jetcas.2023.3238295","pdf_url":null,"source":{"id":"https://openalex.org/S142323794","display_name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","issn_l":"2156-3357","issn":["2156-3357","2156-3365"],"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 on Emerging and Selected Topics in Circuits and Systems","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/A5001880808","display_name":"Osama Yousuf","orcid":"https://orcid.org/0000-0002-7123-3982"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Osama Yousuf","raw_affiliation_strings":["Electrical and Computer Engineering Department, George Washington University, Washington, DC, USA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, George Washington University, Washington, DC, USA","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065630488","display_name":"Imtiaz Hossen","orcid":"https://orcid.org/0000-0002-2931-0518"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Imtiaz Hossen","raw_affiliation_strings":["Electrical and Computer Engineering Department, George Washington University, Washington, DC, USA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, George Washington University, Washington, DC, USA","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008976251","display_name":"Matthew W. Daniels","orcid":"https://orcid.org/0000-0002-3390-4714"},"institutions":[{"id":"https://openalex.org/I1321296531","display_name":"National Institute of Standards and Technology","ror":"https://ror.org/05xpvk416","country_code":"US","type":"funder","lineage":["https://openalex.org/I1321296531","https://openalex.org/I1343035065"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew W. Daniels","raw_affiliation_strings":["National Institute of Standards and Technology, Gaithersburg, MD, USA"],"affiliations":[{"raw_affiliation_string":"National Institute of Standards and Technology, Gaithersburg, MD, USA","institution_ids":["https://openalex.org/I1321296531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043422237","display_name":"Martin Lueker-Boden","orcid":"https://orcid.org/0000-0002-4603-7023"},"institutions":[{"id":"https://openalex.org/I4210121352","display_name":"Western Digital (United States)","ror":"https://ror.org/02hqwnx33","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121352"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Martin Lueker-Boden","raw_affiliation_strings":["Western Digital Technologies, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"Western Digital Technologies, San Jose, CA, USA","institution_ids":["https://openalex.org/I4210121352"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058811779","display_name":"Andrew Dienstfrey","orcid":"https://orcid.org/0000-0002-5461-8741"},"institutions":[{"id":"https://openalex.org/I1321296531","display_name":"National Institute of Standards and Technology","ror":"https://ror.org/05xpvk416","country_code":"US","type":"funder","lineage":["https://openalex.org/I1321296531","https://openalex.org/I1343035065"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrew Dienstfrey","raw_affiliation_strings":["National Institute of Standards and Technology, Boulder, CO, USA"],"affiliations":[{"raw_affiliation_string":"National Institute of Standards and Technology, Boulder, CO, USA","institution_ids":["https://openalex.org/I1321296531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077532268","display_name":"Gina C. Adam","orcid":"https://orcid.org/0000-0003-0027-1145"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gina C. Adam","raw_affiliation_strings":["Electrical and Computer Engineering Department, George Washington University, Washington, DC, USA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, George Washington University, Washington, DC, USA","institution_ids":["https://openalex.org/I193531525"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5001880808"],"corresponding_institution_ids":["https://openalex.org/I193531525"],"apc_list":null,"apc_paid":null,"fwci":0.6686,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.66918539,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":"13","issue":"1","first_page":"382","last_page":"394"},"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":0.9997000098228455,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/resistive-random-access-memory","display_name":"Resistive random-access memory","score":0.757766842842102},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7260987162590027},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.655998945236206},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.6371480822563171},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5065509080886841},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.46717625856399536},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.415763795375824},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.375776469707489},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3494887351989746},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3238011598587036},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.16352394223213196},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12696877121925354},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.1170969307422638}],"concepts":[{"id":"https://openalex.org/C182019814","wikidata":"https://www.wikidata.org/wiki/Q1143830","display_name":"Resistive random-access memory","level":3,"score":0.757766842842102},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7260987162590027},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.655998945236206},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.6371480822563171},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5065509080886841},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.46717625856399536},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.415763795375824},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.375776469707489},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3494887351989746},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3238011598587036},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.16352394223213196},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12696877121925354},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.1170969307422638},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jetcas.2023.3238295","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jetcas.2023.3238295","pdf_url":null,"source":{"id":"https://openalex.org/S142323794","display_name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","issn_l":"2156-3357","issn":["2156-3357","2156-3365"],"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 on Emerging and Selected Topics in Circuits and Systems","raw_type":"journal-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":[{"id":"https://openalex.org/G7810178898","display_name":null,"funder_award_id":"ECNS21932N","funder_id":"https://openalex.org/F4320307793","funder_display_name":"Western Digital"},{"id":"https://openalex.org/G8687009354","display_name":null,"funder_award_id":"70NANB22H018","funder_id":"https://openalex.org/F4320332178","funder_display_name":"National Institute of Standards and Technology"}],"funders":[{"id":"https://openalex.org/F4320307793","display_name":"Western Digital","ror":"https://ror.org/02hqwnx33"},{"id":"https://openalex.org/F4320309515","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67"},{"id":"https://openalex.org/F4320332178","display_name":"National Institute of Standards and Technology","ror":"https://ror.org/05xpvk416"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1937359183","https://openalex.org/W1997629593","https://openalex.org/W2007339694","https://openalex.org/W2008697864","https://openalex.org/W2041984721","https://openalex.org/W2051209243","https://openalex.org/W2125769353","https://openalex.org/W2307193480","https://openalex.org/W2530591187","https://openalex.org/W2535534962","https://openalex.org/W2607144874","https://openalex.org/W2740220207","https://openalex.org/W2750916325","https://openalex.org/W2774817518","https://openalex.org/W2782791387","https://openalex.org/W2891777323","https://openalex.org/W2905425616","https://openalex.org/W2949259459","https://openalex.org/W2949676527","https://openalex.org/W2950548534","https://openalex.org/W2960778947","https://openalex.org/W2969812992","https://openalex.org/W2973064692","https://openalex.org/W3001684375","https://openalex.org/W3006299183","https://openalex.org/W3009436876","https://openalex.org/W3015980402","https://openalex.org/W3037050967","https://openalex.org/W3045934648","https://openalex.org/W3082899001","https://openalex.org/W3094874291","https://openalex.org/W3104147253","https://openalex.org/W3125908175","https://openalex.org/W3176964228","https://openalex.org/W3205515946","https://openalex.org/W3215918518","https://openalex.org/W4223507763","https://openalex.org/W4254504754","https://openalex.org/W4281628293","https://openalex.org/W6638783484","https://openalex.org/W6766978945"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W2750384547","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W1908107260","https://openalex.org/W1952005211","https://openalex.org/W2048582679","https://openalex.org/W2782226720"],"abstract_inverted_index":{"Emerging":[0],"technologies":[1],"based":[2,199],"on":[3,177,188,193,200],"resistive":[4],"switching":[5,215],"(ReRAM)":[6],"devices":[7,35,107,157],"promise":[8],"to":[9,65,85,103,120,182],"improve":[10],"the":[11,55,77,113,178,184,218,225],"speed":[12],"and":[13,38,69,134,150,166,213,249],"energy":[14],"efficiency":[15],"of":[16,57,73,79,115,128,211],"next":[17],"generation":[18],"machine":[19],"learning":[20],"accelerators,":[21],"but":[22],"further":[23],"research":[24],"is":[25,36,60,92,168],"required":[26],"for":[27,108,148,158,227],"achieving":[28],"commercial":[29],"maturity.":[30],"System-level":[31],"prototyping":[32],"with":[33,145,235],"emerging":[34,105],"costly,":[37],"algorithmic":[39],"investigations":[40,230],"require":[41],"hardware":[42,109,185],"neural":[43,80,250],"network":[44,81,205,251],"modeling":[45,58,88,90,131,243],"which":[46],"often":[47],"deviates":[48],"from":[49,154,203],"experimental":[50,151],"reality.":[51],"In":[52],"this":[53,67],"work,":[54],"concept":[56],"bias":[59,91,244],"proposed":[61,222],"as":[62,238,240],"a":[63,99,135,189,208],"way":[64],"quantify":[66],"deviation":[68],"support":[70],"reliable":[71],"evaluation":[72],"device":[74,87,122,162,179,197,219,233],"populations":[75],"in":[76,118,217,245],"context":[78],"algorithms.":[82],"While":[83],"applicable":[84],"other":[86],"techniques,":[89],"investigated":[93],"here":[94],"using":[95,142],"jump":[96,129],"tables":[97,117],"-":[98,139],"promising":[100],"physics-less":[101],"technique":[102],"model":[104],"memory":[106],"networks.":[110],"Questions":[111],"about":[112],"fidelity":[114],"these":[116,171],"relation":[119],"stochastic":[121],"behavior":[123],"are":[124,140,164],"answered.":[125],"Two":[126],"methods":[127],"table":[130],"\u2013":[132],"binning":[133,138,201],"novel":[136],"Optuna-optimized":[137],"explored":[141],"synthetic":[143],"data":[144,152],"known":[146],"distributions":[147],"benchmarking":[149],"obtained":[153],"TiOx":[155],"ReRAM":[156],"practical":[159],"testing.":[160],"Novel":[161],"metrics":[163,172],"proposed,":[165],"it":[167],"shown":[169],"that":[170,196],"can":[173],"present":[174],"crucial":[175],"insights":[176],"population":[180],"prior":[181],"training":[183],"network.":[186],"Results":[187],"multi-layer":[190],"perceptron":[191],"trained":[192],"MNIST":[194],"show":[195],"models":[198,234],"deviate":[202],"target":[204],"accuracy":[206],"at":[207],"low":[209],"number":[210],"points":[212],"high":[214],"noise":[216],"dataset.":[220],"The":[221],"approach":[223],"opens":[224],"possibility":[226],"device-algorithm":[228],"co-design":[229],"into":[231],"statistical":[232],"better":[236],"performance,":[237],"well":[239],"experimentally":[241],"verified":[242],"different":[246],"in-memory":[247],"computing":[248],"architectures.":[252]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
