{"id":"https://openalex.org/W3016678144","doi":"https://doi.org/10.1109/lascas45839.2020.9069026","title":"Reliability Evaluation of Compressed Deep Learning Models","display_name":"Reliability Evaluation of Compressed Deep Learning Models","publication_year":2020,"publication_date":"2020-02-01","ids":{"openalex":"https://openalex.org/W3016678144","doi":"https://doi.org/10.1109/lascas45839.2020.9069026","mag":"3016678144"},"language":"en","primary_location":{"id":"doi:10.1109/lascas45839.2020.9069026","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lascas45839.2020.9069026","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 11th Latin American Symposium on Circuits &amp; Systems (LASCAS)","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/A5029875406","display_name":"Brunno F. Goldstein","orcid":"https://orcid.org/0000-0002-5450-3557"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Brunno F. Goldstein","raw_affiliation_strings":["Universidade Federal do Rio de Janeiro (UFRJ), Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade Federal do Rio de Janeiro (UFRJ), Brazil","institution_ids":["https://openalex.org/I122140584"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065143008","display_name":"Sudarshan Srinivasan","orcid":"https://orcid.org/0009-0002-8662-5820"},"institutions":[{"id":"https://openalex.org/I4210146682","display_name":"Intel (India)","ror":"https://ror.org/04f2n1245","country_code":"IN","type":"company","lineage":["https://openalex.org/I1343180700","https://openalex.org/I4210146682"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sudarshan Srinivasan","raw_affiliation_strings":["Intel Labs, India"],"affiliations":[{"raw_affiliation_string":"Intel Labs, India","institution_ids":["https://openalex.org/I4210146682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052810907","display_name":"Dipankar Das","orcid":"https://orcid.org/0000-0002-8110-9344"},"institutions":[{"id":"https://openalex.org/I4210146682","display_name":"Intel (India)","ror":"https://ror.org/04f2n1245","country_code":"IN","type":"company","lineage":["https://openalex.org/I1343180700","https://openalex.org/I4210146682"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Dipankar Das","raw_affiliation_strings":["Intel Labs, India"],"affiliations":[{"raw_affiliation_string":"Intel Labs, India","institution_ids":["https://openalex.org/I4210146682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023588595","display_name":"Kunal Banerjee","orcid":"https://orcid.org/0000-0002-0605-630X"},"institutions":[{"id":"https://openalex.org/I4210146682","display_name":"Intel (India)","ror":"https://ror.org/04f2n1245","country_code":"IN","type":"company","lineage":["https://openalex.org/I1343180700","https://openalex.org/I4210146682"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kunal Banerjee","raw_affiliation_strings":["Intel Labs, India"],"affiliations":[{"raw_affiliation_string":"Intel Labs, India","institution_ids":["https://openalex.org/I4210146682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063304494","display_name":"Leandro Santiago","orcid":"https://orcid.org/0000-0002-3631-8761"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Leandro Santiago","raw_affiliation_strings":["Universidade Federal do Rio de Janeiro (UFRJ), Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade Federal do Rio de Janeiro (UFRJ), Brazil","institution_ids":["https://openalex.org/I122140584"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073655706","display_name":"Victor C. Ferreira","orcid":"https://orcid.org/0000-0001-6238-5856"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Victor C. Ferreira","raw_affiliation_strings":["Universidade Federal do Rio de Janeiro (UFRJ), Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade Federal do Rio de Janeiro (UFRJ), Brazil","institution_ids":["https://openalex.org/I122140584"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015942916","display_name":"Alexandre S. Nery","orcid":"https://orcid.org/0000-0002-3199-4322"},"institutions":[{"id":"https://openalex.org/I150729083","display_name":"Universidade de Bras\u00edlia","ror":"https://ror.org/02xfp8v59","country_code":"BR","type":"education","lineage":["https://openalex.org/I150729083"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Alexandre S. Nery","raw_affiliation_strings":["Universidade de Bras\u00b4\u0131lia (UnB), Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade de Bras\u00b4\u0131lia (UnB), Brazil","institution_ids":["https://openalex.org/I150729083"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054064879","display_name":"Sandip Kundu","orcid":"https://orcid.org/0000-0001-8221-3824"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sandip Kundu","raw_affiliation_strings":["University of Massachusetts, Amherst, US"],"affiliations":[{"raw_affiliation_string":"University of Massachusetts, Amherst, US","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033447808","display_name":"Felipe M. G. Fran\u00e7a","orcid":"https://orcid.org/0000-0002-8980-6208"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Felipe M. G. Franca","raw_affiliation_strings":["Universidade Federal do Rio de Janeiro (UFRJ), Brazil"],"affiliations":[{"raw_affiliation_string":"Universidade Federal do Rio de Janeiro (UFRJ), Brazil","institution_ids":["https://openalex.org/I122140584"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5029875406"],"corresponding_institution_ids":["https://openalex.org/I122140584"],"apc_list":null,"apc_paid":null,"fwci":2.3313,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.90574942,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9998000264167786,"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":0.9955000281333923,"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/computer-science","display_name":"Computer science","score":0.8250501155853271},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7025262713432312},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6995322704315186},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.6902223229408264},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6429640054702759},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5536280870437622},{"id":"https://openalex.org/keywords/resilience","display_name":"Resilience (materials science)","score":0.5414618849754333},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5282358527183533},{"id":"https://openalex.org/keywords/floating-point","display_name":"Floating point","score":0.5089111328125},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48030155897140503},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.48015135526657104},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.46525153517723083},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4045228362083435},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32956385612487793},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3262494206428528}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8250501155853271},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7025262713432312},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6995322704315186},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.6902223229408264},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6429640054702759},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5536280870437622},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.5414618849754333},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5282358527183533},{"id":"https://openalex.org/C84211073","wikidata":"https://www.wikidata.org/wiki/Q117879","display_name":"Floating point","level":2,"score":0.5089111328125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48030155897140503},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.48015135526657104},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.46525153517723083},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4045228362083435},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32956385612487793},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3262494206428528},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","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},{"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lascas45839.2020.9069026","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lascas45839.2020.9069026","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 11th Latin American Symposium on Circuits &amp; Systems (LASCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1821462560","https://openalex.org/W1841592590","https://openalex.org/W1981276685","https://openalex.org/W1987856988","https://openalex.org/W1994530392","https://openalex.org/W2117539524","https://openalex.org/W2125169487","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2395566064","https://openalex.org/W2513554817","https://openalex.org/W2554302513","https://openalex.org/W2736123080","https://openalex.org/W2764043458","https://openalex.org/W2767260595","https://openalex.org/W2809188712","https://openalex.org/W2899771611","https://openalex.org/W2963122961","https://openalex.org/W2963374099","https://openalex.org/W2963396341","https://openalex.org/W2964082701","https://openalex.org/W2964299589","https://openalex.org/W3208968887","https://openalex.org/W4245199738","https://openalex.org/W4252174618","https://openalex.org/W4297813615","https://openalex.org/W4299322810","https://openalex.org/W6638523607","https://openalex.org/W6638783484","https://openalex.org/W6677580257","https://openalex.org/W6684191040","https://openalex.org/W6711716804","https://openalex.org/W6741285702","https://openalex.org/W6745148473","https://openalex.org/W6746698991","https://openalex.org/W6756040250","https://openalex.org/W6803236535"],"related_works":["https://openalex.org/W2373300491","https://openalex.org/W1212596013","https://openalex.org/W3009327594","https://openalex.org/W2803935332","https://openalex.org/W3183118997","https://openalex.org/W3214410901","https://openalex.org/W3204400881","https://openalex.org/W3204296682","https://openalex.org/W2917767146","https://openalex.org/W2160477146"],"abstract_inverted_index":{"Neural":[0],"networks":[1,94],"are":[2,27,62],"becoming":[3],"deeper":[4],"and":[5,13,24,43,68,113,151],"more":[6,64,72,145],"complex,":[7],"making":[8],"it":[9],"harder":[10],"to":[11,31,66,74,131,147,153,166,169],"store":[12],"process":[14],"such":[15],"applications":[16],"on":[17,89],"systems":[18],"with":[19,40,109,157],"limited":[20],"resources.":[21],"Model":[22],"pruning":[23,119],"data":[25,47],"quantization":[26],"two":[28],"effective":[29,164],"ways":[30],"simplify":[32],"the":[33,38,45,69,98,122,125,132,138,148,176],"necessary":[34],"hardware":[35],"by":[36,127,142],"compressing":[37],"network":[39,70,150],"relevant-only":[41],"nodes":[42,61],"reducing":[44],"required":[46],"precision.":[48],"Such":[49],"optimizations,":[50],"however,":[51],"might":[52],"come":[53],"at":[54],"a":[55,101],"cost":[56],"of":[57,86,100,124],"reliability":[58],"since":[59],"critical":[60],"now":[63],"exposed":[65],"faults":[67,88],"is":[71],"sensitive":[73],"small":[75],"changes.":[76],"In":[77],"this":[78],"work,":[79],"we":[80],"present":[81],"an":[82,163],"extensive":[83],"empirical":[84],"investigation":[85],"transient":[87],"compressed":[90],"deep":[91,170],"convolutional":[92],"neural":[93],"(CNNs).":[95],"We":[96,116],"evaluate":[97],"impact":[99],"single":[102],"bit":[103],"flip":[104],"over":[105],"three":[106],"CNN":[107],"models":[108],"different":[110],"sparsity":[111],"configurations":[112],"integer-only":[114],"quantizations.":[115],"show":[117],"that":[118],"can":[120,136],"increase":[121],"resilience":[123,146,168],"system":[126],"9\u00d7":[128],"when":[129,155],"compared":[130],"dense":[133],"model.":[134],"Quantization":[135],"outperform":[137],"32-bit":[139],"floating-point":[140],"baseline":[141],"adding":[143],"27.4\u00d7":[144],"overall":[149],"up":[152],"108.7\u00d7":[154],"combined":[156],"pruning.":[158],"This":[159],"makes":[160],"model":[161],"compression":[162],"way":[165],"provide":[167],"learning":[171],"workloads":[172],"during":[173],"inference,":[174],"mitigating":[175],"need":[177],"for":[178],"explicit":[179],"error":[180],"correction":[181],"hardware.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
