{"id":"https://openalex.org/W3033788476","doi":"https://doi.org/10.1109/iolts50870.2020.9159704","title":"High-level Modeling of Manufacturing Faults in Deep Neural Network Accelerators","display_name":"High-level Modeling of Manufacturing Faults in Deep Neural Network Accelerators","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3033788476","doi":"https://doi.org/10.1109/iolts50870.2020.9159704","mag":"3033788476"},"language":"en","primary_location":{"id":"doi:10.1109/iolts50870.2020.9159704","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iolts50870.2020.9159704","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 26th International Symposium on On-Line Testing and Robust System Design (IOLTS)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2006.03616","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066094401","display_name":"Shamik Kundu","orcid":"https://orcid.org/0000-0002-5992-8554"},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shamik Kundu","raw_affiliation_strings":["The University of Texas at Dallas, Dallas, Texas"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Texas at Dallas, Dallas, Texas","institution_ids":["https://openalex.org/I162577319"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086566541","display_name":"Ahmet Soyyigit","orcid":null},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ahmet Soyyigit","raw_affiliation_strings":["University of Missouri, Columbia, Missouri"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Missouri, Columbia, Missouri","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069684684","display_name":"Khaza Anuarul Hoque","orcid":"https://orcid.org/0000-0002-1625-6479"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Khaza Anuarul Hoque","raw_affiliation_strings":["University of Missouri, Columbia, Missouri"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Missouri, Columbia, Missouri","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066320524","display_name":"Kanad Basu","orcid":"https://orcid.org/0000-0002-6431-7512"},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kanad Basu","raw_affiliation_strings":["The University of Texas at Dallas, Dallas, Texas"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Texas at Dallas, Dallas, Texas","institution_ids":["https://openalex.org/I162577319"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.2721,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.87998818,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9995999932289124,"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/T11005","display_name":"Radiation Effects in Electronics","score":0.9987999796867371,"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/T10904","display_name":"Embedded Systems Design Techniques","score":0.9979000091552734,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7409177422523499},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7352469563484192},{"id":"https://openalex.org/keywords/matrix-multiplication","display_name":"Matrix multiplication","score":0.637382447719574},{"id":"https://openalex.org/keywords/multiplication","display_name":"Multiplication (music)","score":0.6041489839553833},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5773136019706726},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.534874677658081},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.5197960138320923},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4362831115722656},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4239282011985779},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42056962847709656},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.382479727268219},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.348416268825531},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3289638161659241},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.20498818159103394},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08768370747566223}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7409177422523499},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7352469563484192},{"id":"https://openalex.org/C17349429","wikidata":"https://www.wikidata.org/wiki/Q1049914","display_name":"Matrix multiplication","level":3,"score":0.637382447719574},{"id":"https://openalex.org/C2780595030","wikidata":"https://www.wikidata.org/wiki/Q3860309","display_name":"Multiplication (music)","level":2,"score":0.6041489839553833},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5773136019706726},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.534874677658081},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5197960138320923},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4362831115722656},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4239282011985779},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42056962847709656},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.382479727268219},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.348416268825531},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3289638161659241},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.20498818159103394},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08768370747566223},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/iolts50870.2020.9159704","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iolts50870.2020.9159704","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 26th International Symposium on On-Line Testing and Robust System Design (IOLTS)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2006.03616","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2006.03616","pdf_url":"https://arxiv.org/pdf/2006.03616","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2006.03616","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2006.03616","pdf_url":"https://arxiv.org/pdf/2006.03616","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1430091258","https://openalex.org/W1516256348","https://openalex.org/W1971368502","https://openalex.org/W2005998857","https://openalex.org/W2017369466","https://openalex.org/W2121881146","https://openalex.org/W2134991157","https://openalex.org/W2289252105","https://openalex.org/W2606722458","https://openalex.org/W2964108906","https://openalex.org/W3024621361","https://openalex.org/W6628155437"],"related_works":["https://openalex.org/W3099313426","https://openalex.org/W4287593139","https://openalex.org/W752783541","https://openalex.org/W1506547947","https://openalex.org/W4206811032","https://openalex.org/W2995605830","https://openalex.org/W2086123442","https://openalex.org/W2596457687","https://openalex.org/W3212757063","https://openalex.org/W2093666864"],"abstract_inverted_index":{"The":[0,84,103,136],"advent":[1],"of":[2,9,47,68,100,117,130],"data-driven":[3],"real-time":[4],"applications":[5],"requires":[6],"the":[7,48,78,90,98,109,115,128,133],"implementation":[8],"Deep":[10],"Neural":[11],"Networks":[12],"(DNNs)":[13],"on":[14,147],"Machine":[15],"Learning":[16],"accelerators.":[17],"Google's":[18],"Tensor":[19],"Processing":[20],"Unit":[21],"(TPU)":[22],"is":[23,87,112],"one":[24],"such":[25],"neural":[26,134],"network":[27],"accelerator":[28],"that":[29,108],"uses":[30],"systolic":[31],"array-based":[32],"matrix":[33,49],"multiplication":[34,50],"hardware":[35],"for":[36],"computation":[37],"in":[38,56,74,132],"its":[39],"crux.":[40],"Manufacturing":[41],"faults":[42,70,119],"at":[43],"any":[44],"state":[45],"element":[46],"unit":[51],"can":[52],"cause":[53],"unexpected":[54],"errors":[55],"these":[57],"inference":[58],"networks.":[59],"In":[60],"this":[61],"paper,":[62],"we":[63],"propose":[64],"a":[65,75,148],"formal":[66],"model":[67,86,92,141],"permanent":[69,118],"and":[71,127],"their":[72,123],"propagation":[73],"TPU":[76],"using":[77,89,145],"Discrete-Time":[79],"Markov":[80],"Chain":[81],"(DTMC)":[82],"formalism.":[83],"proposed":[85],"analyzed":[88],"probabilistic":[91],"checking":[93],"technique":[94],"to":[95,114],"reason":[96],"about":[97],"likelihood":[99],"faulty":[101],"outputs.":[102],"obtained":[104],"quantitative":[105],"results":[106],"show":[107],"classification":[110],"accuracy":[111],"sensitive":[113],"type":[116],"as":[120,122],"well":[121],"location,":[124],"bit":[125],"position":[126],"number":[129],"layers":[131],"network.":[135],"conclusions":[137],"from":[138],"our":[139],"theoretical":[140],"have":[142],"been":[143],"validated":[144],"experiments":[146],"digit":[149],"recognition-based":[150],"DNN.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4}],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2020-06-12T00:00:00"}