{"id":"https://openalex.org/W3014017759","doi":"https://doi.org/10.1109/asp-dac47756.2020.9045324","title":"FTT-NAS: Discovering Fault-Tolerant Neural Architecture","display_name":"FTT-NAS: Discovering Fault-Tolerant Neural Architecture","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3014017759","doi":"https://doi.org/10.1109/asp-dac47756.2020.9045324","mag":"3014017759"},"language":"en","primary_location":{"id":"doi:10.1109/asp-dac47756.2020.9045324","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asp-dac47756.2020.9045324","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC)","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/A5077459287","display_name":"Wenshuo Li","orcid":"https://orcid.org/0000-0001-5638-2114"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenshuo Li","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086217226","display_name":"Xuefei Ning","orcid":"https://orcid.org/0000-0003-2209-8312"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuefei Ning","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034555845","display_name":"Guangjun Ge","orcid":"https://orcid.org/0000-0001-5855-6480"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangjun Ge","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiaoming Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoming Chen","raw_affiliation_strings":["State Key Laboratory of Computer Architecture, Institute of Computing Technology, CAS, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Computer Architecture, Institute of Computing Technology, CAS, Beijing, China","institution_ids":["https://openalex.org/I4210090176"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100445368","display_name":"Yu Wang","orcid":"https://orcid.org/0000-0003-3511-0288"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Wang","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023755254","display_name":"Huazhong Yang","orcid":"https://orcid.org/0000-0003-2421-353X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huazhong Yang","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.1977,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.95062827,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9980999827384949,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.989799976348877,"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":0.7033427357673645},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6730452179908752},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.6156675219535828},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6022754311561584},{"id":"https://openalex.org/keywords/fault-tolerance","display_name":"Fault tolerance","score":0.5828258991241455},{"id":"https://openalex.org/keywords/edge-computing","display_name":"Edge computing","score":0.5821150541305542},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.5326672792434692},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5149127244949341},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.4856930673122406},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.4817385971546173},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45339563488960266},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.45137444138526917},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.4396998882293701},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.42251232266426086},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4187558889389038},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.3479234576225281},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33403486013412476}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7033427357673645},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6730452179908752},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.6156675219535828},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6022754311561584},{"id":"https://openalex.org/C63540848","wikidata":"https://www.wikidata.org/wiki/Q3140932","display_name":"Fault tolerance","level":2,"score":0.5828258991241455},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.5821150541305542},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.5326672792434692},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5149127244949341},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.4856930673122406},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.4817385971546173},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45339563488960266},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.45137444138526917},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.4396998882293701},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.42251232266426086},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4187558889389038},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3479234576225281},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33403486013412476},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/asp-dac47756.2020.9045324","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asp-dac47756.2020.9045324","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1903029394","https://openalex.org/W2081215702","https://openalex.org/W2119717200","https://openalex.org/W2152839228","https://openalex.org/W2194775991","https://openalex.org/W2276486856","https://openalex.org/W2524428287","https://openalex.org/W2553303224","https://openalex.org/W2608277226","https://openalex.org/W2626719825","https://openalex.org/W2749187408","https://openalex.org/W2798273231","https://openalex.org/W2807835252","https://openalex.org/W2810075754","https://openalex.org/W2902083778","https://openalex.org/W2903175372","https://openalex.org/W2943759410","https://openalex.org/W2943768976","https://openalex.org/W2945387900","https://openalex.org/W2946522000","https://openalex.org/W2946801000","https://openalex.org/W2951104886","https://openalex.org/W2962746461","https://openalex.org/W2963163009","https://openalex.org/W2963263347","https://openalex.org/W2963374479","https://openalex.org/W2963674932","https://openalex.org/W2963918968","https://openalex.org/W2964121744","https://openalex.org/W2964294659","https://openalex.org/W2964331719","https://openalex.org/W2965658867","https://openalex.org/W2967733054","https://openalex.org/W3106250896","https://openalex.org/W3118608800","https://openalex.org/W4212788319","https://openalex.org/W4285719527","https://openalex.org/W4297778814","https://openalex.org/W4300687381","https://openalex.org/W4300687870","https://openalex.org/W6631190155","https://openalex.org/W6726497184","https://openalex.org/W6727208969","https://openalex.org/W6729956949","https://openalex.org/W6737372364","https://openalex.org/W6745728296","https://openalex.org/W6748057086","https://openalex.org/W6752515464","https://openalex.org/W6753303928","https://openalex.org/W6756496882","https://openalex.org/W6756944905","https://openalex.org/W6763064925","https://openalex.org/W6785652829","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W4322761281","https://openalex.org/W4238233472","https://openalex.org/W4313463218","https://openalex.org/W4312996489","https://openalex.org/W3111395152","https://openalex.org/W4313526662","https://openalex.org/W3106131444","https://openalex.org/W3216099748","https://openalex.org/W4205963435","https://openalex.org/W4319161913"],"abstract_inverted_index":{"With":[0],"the":[1,18,21,27,58,82,102,137,151,174,190,203],"fast":[2],"evolvement":[3],"of":[4,37,61,101],"deep-learning":[5],"specific":[6],"embedded":[7],"computing":[8,67],"systems,":[9],"applications":[10,71],"powered":[11],"by":[12,43,219],"deep":[13],"learning":[14],"are":[15,34,121,197],"moving":[16],"from":[17],"cloud":[19],"to":[20,112,123,140,201],"edge.":[22],"When":[23],"deploying":[24,62],"NNs":[25],"onto":[26],"edge":[28,66,128],"devices":[29,68],"under":[30,177],"complex":[31],"environments,":[32],"there":[33,196],"various":[35,124],"types":[36],"possible":[38],"faults:":[39],"soft":[40],"errors":[41],"caused":[42],"atmospheric":[44],"neutrons":[45],"and":[46,54,86,90,155,168,215],"radioactive":[47],"impurities,":[48],"voltage":[49],"instability,":[50],"aging,":[51],"temperature":[52],"variations,":[53],"malicious":[55],"attackers.":[56],"Thus":[57],"safety":[59],"risk":[60],"neural":[63,95,116,212],"networks":[64],"at":[65],"in":[69,126,136],"safety-critic":[70],"is":[72,172],"now":[73],"drawing":[74],"much":[75],"attention.":[76],"In":[77],"this":[78],"paper,":[79],"we":[80,105,131,145,193,216],"implement":[81],"random":[83],"bit-flip,":[84],"Gaussian,":[85],"Salt-and-Pepper":[87],"fault":[88,180],"models":[89],"establish":[91],"a":[92,178,220],"multi-objective":[93],"fault-tolerant":[94,133,211],"architecture":[96,153,213],"search":[97,138],"framework.":[98],"On":[99],"top":[100],"NAS":[103],"framework,":[104],"propose":[106],"Fault-Tolerant":[107],"Neural":[108],"Architecture":[109],"Search":[110],"(FT-NAS)":[111],"automatically":[113],"discover":[114],"convolutional":[115],"network":[117],"(CNN)":[118],"architectures":[119,161,175],"that":[120,150,195],"reliable":[122],"faults":[125],"nowadays":[127],"devices.":[129],"Then":[130],"incorporate":[132],"training":[134],"(FTT)":[135],"process":[139],"achieve":[141],"better":[142],"results,":[143],"which":[144],"called":[146],"FTT-NAS.":[147],"Experiments":[148],"show":[149],"discovered":[152,191],"FT-NAS-Net":[154],"FTT-NAS-Net":[156],"outperform":[157],"other":[158,186],"hand-designed":[159],"baseline":[160],"(58.1%/86.6%":[162],"VS.":[163],"10.0%/52.2%),":[164],"with":[165],"comparable":[166],"FLOPs":[167],"less":[169],"parameters.":[170],"What":[171],"more,":[173],"trained":[176],"single":[179],"model":[181],"can":[182,208],"also":[183],"defend":[184],"against":[185],"faults.":[187],"By":[188],"inspecting":[189],"architecture,":[192],"find":[194],"redundant":[198],"connections":[199],"learned":[200],"protect":[202],"sensitive":[204],"paths.":[205],"This":[206],"insight":[207],"guide":[209],"future":[210],"design,":[214],"verify":[217],"it":[218],"modification":[221],"on":[222],"ResNet-20-ResNet-M.":[223]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
