{"id":"https://openalex.org/W2989817299","doi":"https://doi.org/10.23919/date48585.2020.9116571","title":"FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation","display_name":"FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W2989817299","doi":"https://doi.org/10.23919/date48585.2020.9116571","mag":"2989817299"},"language":"en","primary_location":{"id":"doi:10.23919/date48585.2020.9116571","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date48585.2020.9116571","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1912.00941","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017443590","display_name":"Le-Ha Hoang","orcid":null},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":true,"raw_author_name":"Le-Ha Hoang","raw_affiliation_strings":["Technische Universit\u00e4t Wien (TU Wien), Vienna, Austria"],"affiliations":[{"raw_affiliation_string":"Technische Universit\u00e4t Wien (TU Wien), Vienna, Austria","institution_ids":["https://openalex.org/I145847075"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100647460","display_name":"Muhammad Abdullah Hanif","orcid":"https://orcid.org/0000-0001-9841-6132"},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Muhammad Abdullah Hanif","raw_affiliation_strings":["Technische Universit\u00e4t Wien (TU Wien), Vienna, Austria"],"affiliations":[{"raw_affiliation_string":"Technische Universit\u00e4t Wien (TU Wien), Vienna, Austria","institution_ids":["https://openalex.org/I145847075"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005190949","display_name":"Muhammad Shafique","orcid":"https://orcid.org/0000-0002-2607-8135"},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Muhammad Shafique","raw_affiliation_strings":["Technische Universit\u00e4t Wien (TU Wien), Vienna, Austria","Vienna University of Technology"],"affiliations":[{"raw_affiliation_string":"Technische Universit\u00e4t Wien (TU Wien), Vienna, Austria","institution_ids":["https://openalex.org/I145847075"]},{"raw_affiliation_string":"Vienna University of Technology","institution_ids":["https://openalex.org/I145847075"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5017443590"],"corresponding_institution_ids":["https://openalex.org/I145847075"],"apc_list":null,"apc_paid":null,"fwci":0.5485,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.72207715,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1241","last_page":"1246"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9994000196456909,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9994000196456909,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9965999722480774,"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"}},{"id":"https://openalex.org/T11005","display_name":"Radiation Effects in Electronics","score":0.9878000020980835,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/resilience","display_name":"Resilience (materials science)","score":0.8290541172027588},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7420088052749634},{"id":"https://openalex.org/keywords/fault-tolerance","display_name":"Fault tolerance","score":0.6243460774421692},{"id":"https://openalex.org/keywords/clipping","display_name":"Clipping (morphology)","score":0.6000698208808899},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5325739979743958},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.4805058240890503},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.46949222683906555},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.4598378539085388},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.44368964433670044},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.33246681094169617},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3299023509025574},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3247835338115692},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.205989271402359},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06809458136558533}],"concepts":[{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.8290541172027588},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7420088052749634},{"id":"https://openalex.org/C63540848","wikidata":"https://www.wikidata.org/wiki/Q3140932","display_name":"Fault tolerance","level":2,"score":0.6243460774421692},{"id":"https://openalex.org/C2776848632","wikidata":"https://www.wikidata.org/wiki/Q853463","display_name":"Clipping (morphology)","level":2,"score":0.6000698208808899},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5325739979743958},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.4805058240890503},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.46949222683906555},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.4598378539085388},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.44368964433670044},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.33246681094169617},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3299023509025574},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3247835338115692},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.205989271402359},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06809458136558533},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.23919/date48585.2020.9116571","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date48585.2020.9116571","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1912.00941","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1912.00941","pdf_url":"https://arxiv.org/pdf/1912.00941","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"},{"id":"mag:2989817299","is_oa":true,"landing_page_url":"https://arxiv.org/abs/1912.00941","pdf_url":null,"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":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1912.00941","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1912.00941","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1912.00941","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1912.00941","pdf_url":"https://arxiv.org/pdf/1912.00941","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":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2989817299.pdf","grobid_xml":"https://content.openalex.org/works/W2989817299.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1966595743","https://openalex.org/W1974078116","https://openalex.org/W2003144033","https://openalex.org/W2017521824","https://openalex.org/W2033453286","https://openalex.org/W2095705004","https://openalex.org/W2120185818","https://openalex.org/W2163605009","https://openalex.org/W2285660444","https://openalex.org/W2442974303","https://openalex.org/W2604319603","https://openalex.org/W2606722458","https://openalex.org/W2626719825","https://openalex.org/W2795147506","https://openalex.org/W2809188712","https://openalex.org/W2898655385","https://openalex.org/W2899771611","https://openalex.org/W2919115771","https://openalex.org/W2945789436","https://openalex.org/W2950656546","https://openalex.org/W2953212265","https://openalex.org/W2964299589","https://openalex.org/W3118608800","https://openalex.org/W6637373629","https://openalex.org/W6643777650","https://openalex.org/W6674330103","https://openalex.org/W6677580257","https://openalex.org/W6684191040","https://openalex.org/W6756040250","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W3213122565","https://openalex.org/W2946682676","https://openalex.org/W3203656370","https://openalex.org/W3209192379","https://openalex.org/W2954329846","https://openalex.org/W2798751785","https://openalex.org/W3097334049","https://openalex.org/W1537523342","https://openalex.org/W2588726438","https://openalex.org/W2800920215","https://openalex.org/W2347654093","https://openalex.org/W3092082681","https://openalex.org/W2615084988","https://openalex.org/W2147735869","https://openalex.org/W2186247608","https://openalex.org/W2771112233","https://openalex.org/W2944594909","https://openalex.org/W3115673245","https://openalex.org/W1608703393","https://openalex.org/W2962710014"],"abstract_inverted_index":{"Deep":[0],"Neural":[1],"Networks":[2],"(DNNs)":[3],"are":[4,18],"widely":[5],"being":[6],"adopted":[7],"for":[8,148],"safety-critical":[9],"applications,":[10],"e.g.,":[11],"healthcare":[12],"and":[13,143],"autonomous":[14],"driving.":[15],"Inherently,":[16],"they":[17],"considered":[19],"to":[20,62,79,94,116,167,198],"be":[21],"highly":[22],"error-tolerant.":[23],"However,":[24],"recent":[25],"studies":[26],"have":[27,42],"shown":[28],"that":[29,32,126,156],"hardware":[30,63],"faults":[31,64],"impact":[33],"the":[34,69,88,103,119,123,132,141,144,149,162,165,171,180,199],"parameters":[35],"of":[36,59,74,122,131,164,183],"a":[37,54,81,114],"DNN":[38],"(e.g.,":[39,65],"weights)":[40],"can":[41],"drastic":[43],"impacts":[44],"on":[45,140,175],"its":[46],"classification":[47,181],"accuracy.":[48],"In":[49],"this":[50,75,100],"paper,":[51],"we":[52],"perform":[53],"comprehensive":[55],"error":[56,83],"resilience":[57,130,163],"analysis":[58,76],"DNNs":[60,146,166],"subjected":[61],"permanent":[66],"faults)":[67],"in":[68,128,179],"weight":[70],"memory.":[71],"The":[72,152],"outcome":[73],"is":[77],"leveraged":[78],"propose":[80],"novel":[82],"mitigation":[84,158],"technique":[85,139,159,173],"which":[86],"squashes":[87],"high-":[89],"intensity":[90],"faulty":[91],"activation":[92,105,124],"values":[93,121],"alleviate":[95],"their":[96,108],"impact.":[97],"We":[98,111,136],"achieve":[99],"by":[101],"replacing":[102],"unbounded":[104],"functions":[106,125],"with":[107],"clipped":[109],"versions.":[110],"also":[112],"present":[113],"method":[115],"systematically":[117],"define":[118],"clipping":[120],"result":[127],"increased":[129],"networks":[133],"against":[134],"faults.":[135,168],"evaluate":[137],"our":[138,157],"AlexNet":[142],"VGG-16":[145,185],"trained":[147],"CIFAR-10":[150],"dataset.":[151],"experimental":[153],"results":[154],"show":[155],"significantly":[160],"improves":[161],"For":[169],"example,":[170],"proposed":[172],"offers":[174],"average":[176],"68.92%":[177],"improvement":[178],"accuracy":[182],"resilience-optimized":[184],"model":[186],"at":[187],"1":[188],"\u00d7":[189],"10":[190],"<sup":[191],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[192],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">-5</sup>":[193],"fault":[194,204],"rate,":[195],"when":[196],"compared":[197],"base":[200],"network":[201],"without":[202],"any":[203],"mitigation.":[205]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}