{"id":"https://openalex.org/W3013462594","doi":"https://doi.org/10.1109/asp-dac47756.2020.9045134","title":"When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remedies","display_name":"When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remedies","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3013462594","doi":"https://doi.org/10.1109/asp-dac47756.2020.9045134","mag":"3013462594"},"language":"en","primary_location":{"id":"doi:10.1109/asp-dac47756.2020.9045134","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asp-dac47756.2020.9045134","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":"conference-paper","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/A5059093077","display_name":"Zheyu Yan","orcid":"https://orcid.org/0000-0003-1830-606X"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheyu Yan","raw_affiliation_strings":["Zhejiang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000141831","display_name":"Yiyu Shi","orcid":"https://orcid.org/0000-0002-6788-9823"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiyu Shi","raw_affiliation_strings":["University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065071055","display_name":"Wang Liao","orcid":"https://orcid.org/0000-0003-2134-5588"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Wang Liao","raw_affiliation_strings":["Osaka University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Osaka University","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002405139","display_name":"Masanori Hashimoto","orcid":"https://orcid.org/0000-0002-0377-2108"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masanori Hashimoto","raw_affiliation_strings":["Osaka University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Osaka University","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010601964","display_name":"Xichuan Zhou","orcid":"https://orcid.org/0000-0002-3304-3045"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xichuan Zhou","raw_affiliation_strings":["Chongqing University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chongqing University","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054211420","display_name":"Cheng Zhuo","orcid":"https://orcid.org/0000-0002-2610-7522"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Zhuo","raw_affiliation_strings":["Zhejiang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":61,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"163","last_page":"168"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11005","display_name":"Radiation Effects in Electronics","score":0.9998999834060669,"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/T11005","display_name":"Radiation Effects in Electronics","score":0.9998999834060669,"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/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/T12122","display_name":"Physical Unclonable Functions (PUFs) and Hardware Security","score":0.9987999796867371,"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/robustness","display_name":"Robustness (evolution)","score":0.7756572365760803},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7711461782455444},{"id":"https://openalex.org/keywords/single-event-upset","display_name":"Single event upset","score":0.5860303640365601},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5341953635215759},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.4111884534358978},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38016098737716675},{"id":"https://openalex.org/keywords/static-random-access-memory","display_name":"Static random-access memory","score":0.3660697340965271},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3346308469772339},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.3287954330444336},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.17344167828559875},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10297736525535583}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7756572365760803},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7711461782455444},{"id":"https://openalex.org/C2780073065","wikidata":"https://www.wikidata.org/wiki/Q1476733","display_name":"Single event upset","level":3,"score":0.5860303640365601},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5341953635215759},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4111884534358978},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38016098737716675},{"id":"https://openalex.org/C68043766","wikidata":"https://www.wikidata.org/wiki/Q267416","display_name":"Static random-access memory","level":2,"score":0.3660697340965271},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3346308469772339},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.3287954330444336},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.17344167828559875},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10297736525535583},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/asp-dac47756.2020.9045134","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asp-dac47756.2020.9045134","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":[{"id":"https://openalex.org/G8235577256","display_name":"Muon-induced soft error evaluation platform: future prediction based on measurement and simulation","funder_award_id":"19H05664","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1799366690","https://openalex.org/W1945616565","https://openalex.org/W2074673023","https://openalex.org/W2085992264","https://openalex.org/W2094756095","https://openalex.org/W2108598243","https://openalex.org/W2120185818","https://openalex.org/W2167677193","https://openalex.org/W2194775991","https://openalex.org/W2214352687","https://openalex.org/W2603451662","https://openalex.org/W2763915504","https://openalex.org/W2768176179","https://openalex.org/W2798282242","https://openalex.org/W2799462322","https://openalex.org/W2805584843","https://openalex.org/W2809188712","https://openalex.org/W2809555641","https://openalex.org/W2885804315","https://openalex.org/W2903140524","https://openalex.org/W2911884654","https://openalex.org/W2945804086","https://openalex.org/W2962953210","https://openalex.org/W2963207607","https://openalex.org/W2988973041","https://openalex.org/W3118608800","https://openalex.org/W3121413886","https://openalex.org/W4252174618","https://openalex.org/W6637373629","https://openalex.org/W6638444622","https://openalex.org/W6640425456","https://openalex.org/W6674479107","https://openalex.org/W6735870940","https://openalex.org/W6745100626","https://openalex.org/W6751836886"],"related_works":["https://openalex.org/W3208260600","https://openalex.org/W2065552285","https://openalex.org/W3003557214","https://openalex.org/W1493283943","https://openalex.org/W4381549462","https://openalex.org/W3156329500","https://openalex.org/W2387824216","https://openalex.org/W19802766","https://openalex.org/W3024449993","https://openalex.org/W2617585808"],"abstract_inverted_index":{"Deep":[0],"Neural":[1],"Network":[2],"has":[3],"proved":[4],"its":[5],"potential":[6],"in":[7,22,123],"various":[8,40],"perception":[9,33],"tasks":[10],"and":[11,19,80,108,134],"hence":[12],"become":[13],"an":[14],"appealing":[15],"option":[16],"for":[17,93,113,162],"interpretation":[18],"data":[20],"processing":[21],"security":[23],"sensitive":[24],"systems.":[25],"However,":[26],"security-sensitive":[27],"systems":[28],"demand":[29],"not":[30],"only":[31],"high":[32],"performance,":[34],"but":[35],"also":[36],"design":[37],"robustness":[38,48,91,129],"under":[39],"circumstances.":[41],"Unlike":[42],"prior":[43],"works":[44],"that":[45],"study":[46],"network":[47,107,133,137],"from":[49,54,151,158],"software":[50],"level,":[51],"we":[52,143],"investigate":[53],"hardware":[55],"perspective":[56],"about":[57],"the":[58,75,83,90,94,103,110,114,131],"impact":[59,115,135],"of":[60,78,85,105,116,121,136],"Single":[61],"Event":[62],"Upset":[63],"(SEU)":[64],"induced":[65],"parameter":[66],"perturbation":[67],"(SIPP)":[68],"on":[69,118,140],"neural":[70],"networks.":[71],"We":[72,96],"systematically":[73],"define":[74],"fault":[76],"models":[77],"SEU":[79],"then":[81,98],"provide":[82],"definition":[84],"sensitivity":[86],"to":[87,100,148,160],"SIPP":[88,117],"as":[89],"measure":[92],"network.":[95],"are":[97],"able":[99],"analytically":[101],"explore":[102],"weakness":[104],"a":[106,124],"summarize":[109],"key":[111],"findings":[112],"different":[119],"types":[120],"bits":[122],"floating":[125],"point":[126],"parameter,":[127],"layer-wise":[128],"within":[130],"same":[132],"depth.":[138],"Based":[139],"those":[141],"findings,":[142],"propose":[144],"two":[145],"remedy":[146],"solutions":[147],"protect":[149],"DNNs":[150],"SIPPs,":[152],"which":[153],"can":[154],"mitigate":[155],"accuracy":[156],"degradation":[157],"28%":[159],"0.27%":[161],"ResNet":[163],"with":[164],"merely":[165],"0.24-bit":[166],"SRAM":[167],"area":[168],"overhead":[169],"per":[170],"parameter.":[171]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":7}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
