{"id":"https://openalex.org/W7147641365","doi":"https://doi.org/10.1109/lascas67804.2026.11457175","title":"Investigating the Impact of Soft Errors in GPU-Accelerated Sparse DNNs","display_name":"Investigating the Impact of Soft Errors in GPU-Accelerated Sparse DNNs","publication_year":2026,"publication_date":"2026-02-24","ids":{"openalex":"https://openalex.org/W7147641365","doi":"https://doi.org/10.1109/lascas67804.2026.11457175"},"language":null,"primary_location":{"id":"doi:10.1109/lascas67804.2026.11457175","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lascas67804.2026.11457175","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 17th Latin America Symposium on Circuits and System (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":null,"display_name":"Mohammad Hasan Ahmadilivani","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mohammad Hasan Ahmadilivani","raw_affiliation_strings":["Tallinn University of Technology,Department of Computer Systems"],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Josie E. Rodriguez Condia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Josie E. Rodriguez Condia","raw_affiliation_strings":["Politecnico di Torino,Department of Control and Computer Engineering (DAUIN)"],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Maksim Jenihhin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maksim Jenihhin","raw_affiliation_strings":["Tallinn University of Technology,Department of Computer Systems"],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Matteo Sonza Reorda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matteo Sonza Reorda","raw_affiliation_strings":["Politecnico di Torino,Department of Control and Computer Engineering (DAUIN)"],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.9631115,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.362199991941452,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.362199991941452,"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":0.13199999928474426,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.12890000641345978,"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/pruning","display_name":"Pruning","score":0.7279999852180481},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5982999801635742},{"id":"https://openalex.org/keywords/resilience","display_name":"Resilience (materials science)","score":0.5331000089645386},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.4943000078201294},{"id":"https://openalex.org/keywords/fault-injection","display_name":"Fault injection","score":0.4580000042915344},{"id":"https://openalex.org/keywords/fault-tolerance","display_name":"Fault tolerance","score":0.42399999499320984},{"id":"https://openalex.org/keywords/mechanism","display_name":"Mechanism (biology)","score":0.4023999869823456},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37929999828338623}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.801800012588501},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7279999852180481},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5982999801635742},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.5331000089645386},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5080000162124634},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.4943000078201294},{"id":"https://openalex.org/C2775928411","wikidata":"https://www.wikidata.org/wiki/Q2041312","display_name":"Fault injection","level":3,"score":0.4580000042915344},{"id":"https://openalex.org/C63540848","wikidata":"https://www.wikidata.org/wiki/Q3140932","display_name":"Fault tolerance","level":2,"score":0.42399999499320984},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.4023999869823456},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37929999828338623},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3686000108718872},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.33820000290870667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.319599986076355},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.31150001287460327},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3050000071525574},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.28600001335144043},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.2752000093460083},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.27239999175071716}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lascas67804.2026.11457175","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lascas67804.2026.11457175","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 17th Latin America Symposium on Circuits and System (LASCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.8124504685401917,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1965069357","https://openalex.org/W2123585936","https://openalex.org/W2964866569","https://openalex.org/W2980186997","https://openalex.org/W3040646053","https://openalex.org/W4312924446","https://openalex.org/W4387064050","https://openalex.org/W4389166750","https://openalex.org/W4390145134","https://openalex.org/W4399666094","https://openalex.org/W4401329586","https://openalex.org/W4401331750","https://openalex.org/W4404918643"],"related_works":[],"abstract_inverted_index":{"The":[0],"widespread":[1],"adoption":[2],"of":[3,20,28,70,95,110,124,150,166,198],"Deep":[4],"Neural":[5,127],"Networks":[6],"(DNNs)":[7],"across":[8],"many":[9],"domains,":[10],"including":[11],"safety-critical":[12],"applications,":[13],"has":[14,84],"been":[15,87],"driven":[16],"by":[17,194],"the":[18,26,67,93,99,107,114,137,148,159,163,183,195],"availability":[19],"sophisticated":[21],"deployment":[22],"algorithms":[23],"that":[24,44,120,173,188],"enable":[25,147],"development":[27],"complex":[29],"and":[30,41,47,144],"effective":[31],"solutions.":[32],"Their":[33],"success":[34],"is":[35,171],"largely":[36],"due":[37,77],"to":[38,78,156,179],"advanced":[39],"compression":[40],"pruning":[42],"techniques":[43],"preserve":[45],"accuracy":[46],"performance,":[48],"as":[49,51,56],"well":[50],"specialized":[52],"hardware":[53],"accelerators,":[54],"such":[55],"GPU":[57,104],"Tensor":[58],"Cores":[59],"(TCs)":[60],"with":[61],"support":[62],"for":[63],"structural":[64],"sparsity.":[65],"However,":[66],"overall":[68,108],"impact":[69,94],"soft":[71,96,180],"errors":[72,97,151,181],"on":[73,106],"sparse":[74,122,142,153,174],"CNN":[75,111,167,199],"applications":[76],"corrupted":[79],"sparsity":[80,101,138,160],"mechanisms":[81],"in":[82,103,141,152],"GPUs":[83,119],"not":[85],"yet":[86],"fully":[88],"investigated.":[89],"This":[90],"work":[91],"evaluates":[92],"impacting":[98],"structured":[100],"mechanism":[102,139,161],"TCs":[105],"operation":[109],"models.":[112,130],"For":[113],"experiments,":[115,158],"we":[116],"use":[117],"real":[118],"implement":[121],"versions":[123],"multiple":[125],"Convolutional":[126],"Network":[128],"(CNN)":[129],"Then,":[131],"several":[132],"fault":[133,164],"injection":[134],"campaigns":[135],"targeting":[136],"(errors":[140],"indices":[143,175],"compressed":[145],"weights)":[146],"characterization":[149],"workloads.":[154,200],"According":[155],"our":[157],"increases":[162],"resilience":[165,197],"deployment.":[168],"Moreover,":[169],"it":[170],"shown":[172],"are":[176,191],"less":[177],"susceptible":[178],"than":[182],"weights.":[184],"These":[185],"results":[186],"indicate":[187],"control":[189],"corruptions":[190],"mostly":[192],"masked":[193],"inherent":[196]},"counts_by_year":[],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2026-02-02T00:00:00"}