{"id":"https://openalex.org/W3108388714","doi":"https://doi.org/10.1109/dft50435.2020.9250872","title":"An Emulation Platform for Evaluating the Reliability of Deep Neural Networks","display_name":"An Emulation Platform for Evaluating the Reliability of Deep Neural Networks","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3108388714","doi":"https://doi.org/10.1109/dft50435.2020.9250872","mag":"3108388714"},"language":"en","primary_location":{"id":"doi:10.1109/dft50435.2020.9250872","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dft50435.2020.9250872","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)","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/A5018567565","display_name":"Corrado De Sio","orcid":"https://orcid.org/0000-0003-4212-3052"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Polytechnic University of Turin","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Corrado De Sio","raw_affiliation_strings":["Dipartimento di Automatica e Informatica, Politecnico di Torino, Turin, Italy"],"affiliations":[{"raw_affiliation_string":"Dipartimento di Automatica e Informatica, Politecnico di Torino, Turin, Italy","institution_ids":["https://openalex.org/I177477856"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004083312","display_name":"Sarah Azimi","orcid":"https://orcid.org/0000-0002-9169-6140"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Polytechnic University of Turin","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Sarah Azimi","raw_affiliation_strings":["Dipartimento di Automatica e Informatica, Politecnico di Torino, Turin, Italy"],"affiliations":[{"raw_affiliation_string":"Dipartimento di Automatica e Informatica, Politecnico di Torino, Turin, Italy","institution_ids":["https://openalex.org/I177477856"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021426042","display_name":"Luca Sterpone","orcid":"https://orcid.org/0000-0002-3080-2560"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Polytechnic University of Turin","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Luca Sterpone","raw_affiliation_strings":["Dipartimento di Automatica e Informatica, Politecnico di Torino, Turin, Italy"],"affiliations":[{"raw_affiliation_string":"Dipartimento di Automatica e Informatica, Politecnico di Torino, Turin, Italy","institution_ids":["https://openalex.org/I177477856"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5018567565"],"corresponding_institution_ids":["https://openalex.org/I177477856"],"apc_list":null,"apc_paid":null,"fwci":1.193,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.83972962,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9987999796867371,"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":0.9980999827384949,"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/reconfigurability","display_name":"Reconfigurability","score":0.9136549234390259},{"id":"https://openalex.org/keywords/emulation","display_name":"Emulation","score":0.8704427480697632},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7400393486022949},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.68250572681427},{"id":"https://openalex.org/keywords/fault-injection","display_name":"Fault injection","score":0.6723470091819763},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6394979953765869},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.5997273921966553},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5895980000495911},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5119718313217163},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.511896014213562},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4739950895309448},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.4575674831867218},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4473445415496826},{"id":"https://openalex.org/keywords/aerospace","display_name":"Aerospace","score":0.44192349910736084},{"id":"https://openalex.org/keywords/automotive-industry","display_name":"Automotive industry","score":0.4122544527053833},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3012775182723999},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14181587100028992},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.09658321738243103},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.07437127828598022}],"concepts":[{"id":"https://openalex.org/C2780149590","wikidata":"https://www.wikidata.org/wiki/Q7302742","display_name":"Reconfigurability","level":2,"score":0.9136549234390259},{"id":"https://openalex.org/C149810388","wikidata":"https://www.wikidata.org/wiki/Q5374873","display_name":"Emulation","level":2,"score":0.8704427480697632},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7400393486022949},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.68250572681427},{"id":"https://openalex.org/C2775928411","wikidata":"https://www.wikidata.org/wiki/Q2041312","display_name":"Fault injection","level":3,"score":0.6723470091819763},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6394979953765869},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.5997273921966553},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5895980000495911},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5119718313217163},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.511896014213562},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4739950895309448},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.4575674831867218},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4473445415496826},{"id":"https://openalex.org/C167740415","wikidata":"https://www.wikidata.org/wiki/Q2876213","display_name":"Aerospace","level":2,"score":0.44192349910736084},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.4122544527053833},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3012775182723999},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14181587100028992},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.09658321738243103},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.07437127828598022},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","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/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dft50435.2020.9250872","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dft50435.2020.9250872","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5099999904632568}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2016053056","https://openalex.org/W2146093477","https://openalex.org/W2146369740","https://openalex.org/W2612354329","https://openalex.org/W2615131227","https://openalex.org/W2618530766","https://openalex.org/W2767260595","https://openalex.org/W2800017313","https://openalex.org/W2809188712","https://openalex.org/W2901848761","https://openalex.org/W2912412330","https://openalex.org/W2919115771","https://openalex.org/W2943759410","https://openalex.org/W2970971581","https://openalex.org/W2981925675","https://openalex.org/W3100321043","https://openalex.org/W4295312788","https://openalex.org/W6766978945","https://openalex.org/W6785474550"],"related_works":["https://openalex.org/W1544665014","https://openalex.org/W2133965417","https://openalex.org/W2159103767","https://openalex.org/W2038220260","https://openalex.org/W2145233434","https://openalex.org/W2291587020","https://openalex.org/W2111105659","https://openalex.org/W2118560622","https://openalex.org/W2062623691","https://openalex.org/W2159677757"],"abstract_inverted_index":{"In":[0,25,78],"recent":[1],"years,":[2],"Deep":[3],"Neural":[4],"Networks":[5],"have":[6],"been":[7,86],"increasingly":[8],"adopted":[9],"by":[10,17],"a":[11,81,89],"wide":[12],"range":[13],"of":[14,39,46,61,64,92,97],"applications":[15],"characterized":[16],"high-reliability":[18],"requirements,":[19],"such":[20],"as":[21],"aerospace":[22],"and":[23,76],"automotive.":[24],"this":[26,79],"paper,":[27],"we":[28],"propose":[29],"an":[30],"FPGA-based":[31],"platform":[32,57],"for":[33],"emulating":[34],"faults":[35,50],"in":[36,104],"the":[37,44,52,59,68,95,98,101,105,112,115],"architecture":[38],"DNNs.":[40,55],"The":[41,56],"approach":[42],"exploits":[43],"reconfigurability":[45],"FPGAs":[47],"to":[48,70,72,111],"mimic":[49],"affecting":[51],"hardware":[53],"implementing":[54],"allows":[58],"emulation":[60],"various":[62],"kinds":[63],"fault":[65,82],"models":[66],"enabling":[67],"possibility":[69],"adapt":[71],"different":[73],"types,":[74],"devices,":[75],"architectures.":[77],"work,":[80],"injection":[83],"campaign":[84],"has":[85],"performed":[87],"on":[88,114],"convolutional":[90],"layer":[91,106],"AlexNet,":[93],"demonstrating":[94],"feasibility":[96],"platform.":[99],"Furthermore,":[100],"errors":[102],"induced":[103],"are":[107],"analyzed":[108],"with":[109],"respect":[110],"impact":[113],"whole":[116],"network":[117],"inference":[118],"classification.":[119]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
