{"id":"https://openalex.org/W4385976015","doi":"https://doi.org/10.1109/mitp.2023.3264849","title":"An Approximate Fault-Tolerance Design for a Convolutional Neural Network Accelerator","display_name":"An Approximate Fault-Tolerance Design for a Convolutional Neural Network Accelerator","publication_year":2023,"publication_date":"2023-07-01","ids":{"openalex":"https://openalex.org/W4385976015","doi":"https://doi.org/10.1109/mitp.2023.3264849"},"language":"en","primary_location":{"id":"doi:10.1109/mitp.2023.3264849","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mitp.2023.3264849","pdf_url":null,"source":{"id":"https://openalex.org/S86192639","display_name":"IT Professional","issn_l":"1520-9202","issn":["1520-9202","1941-045X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IT Professional","raw_type":"journal-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/A5013431962","display_name":"Wenda Wei","orcid":"https://orcid.org/0009-0008-4126-3110"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenda Wei","raw_affiliation_strings":["Jilin University, Changchun, China"],"raw_orcid":"https://orcid.org/0009-0008-4126-3110","affiliations":[{"raw_affiliation_string":"Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082014680","display_name":"Chenyang Wang","orcid":"https://orcid.org/0000-0001-5871-1567"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenyang Wang","raw_affiliation_strings":["Jilin University, Changchun, China"],"raw_orcid":"https://orcid.org/0000-0001-5871-1567","affiliations":[{"raw_affiliation_string":"Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044921954","display_name":"Xinyang Zheng","orcid":"https://orcid.org/0000-0003-3001-6639"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyang Zheng","raw_affiliation_strings":["Jilin University, Changchun, China"],"raw_orcid":"https://orcid.org/0000-0003-3001-6639","affiliations":[{"raw_affiliation_string":"Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033259177","display_name":"Hengshan Yue","orcid":"https://orcid.org/0000-0003-2189-8385"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hengshan Yue","raw_affiliation_strings":["Jilin University, Changchun, China"],"raw_orcid":"https://orcid.org/0000-0003-2189-8385","affiliations":[{"raw_affiliation_string":"Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I194450716"],"apc_list":null,"apc_paid":null,"fwci":0.7559,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.66516318,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"25","issue":"4","first_page":"85","last_page":"90"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11005","display_name":"Radiation Effects in Electronics","score":0.9987000226974487,"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":0.9987000226974487,"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":0.998199999332428,"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":0.9965999722480774,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/fault-tolerance","display_name":"Fault tolerance","score":0.889447808265686},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8061781525611877},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7928054928779602},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.689874529838562},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.5249733328819275},{"id":"https://openalex.org/keywords/error-detection-and-correction","display_name":"Error detection and correction","score":0.49810361862182617},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.48553726077079773},{"id":"https://openalex.org/keywords/triple-modular-redundancy","display_name":"Triple modular redundancy","score":0.46512365341186523},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.4586522579193115},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.44824063777923584},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.39190924167633057},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.35534682869911194},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27814802527427673},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1992557942867279},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.09300285577774048}],"concepts":[{"id":"https://openalex.org/C63540848","wikidata":"https://www.wikidata.org/wiki/Q3140932","display_name":"Fault tolerance","level":2,"score":0.889447808265686},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8061781525611877},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7928054928779602},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.689874529838562},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.5249733328819275},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.49810361862182617},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.48553726077079773},{"id":"https://openalex.org/C196371267","wikidata":"https://www.wikidata.org/wiki/Q3998979","display_name":"Triple modular redundancy","level":3,"score":0.46512365341186523},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.4586522579193115},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.44824063777923584},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.39190924167633057},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.35534682869911194},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27814802527427673},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1992557942867279},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.09300285577774048},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mitp.2023.3264849","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mitp.2023.3264849","pdf_url":null,"source":{"id":"https://openalex.org/S86192639","display_name":"IT Professional","issn_l":"1520-9202","issn":["1520-9202","1941-045X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IT Professional","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G436356148","display_name":null,"funder_award_id":"62272190","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2067523571","https://openalex.org/W2086309060","https://openalex.org/W2442974303","https://openalex.org/W2606722458","https://openalex.org/W2798273231","https://openalex.org/W2946206667","https://openalex.org/W2989569745","https://openalex.org/W3006507836","https://openalex.org/W3084937280","https://openalex.org/W3097528158","https://openalex.org/W3114929349","https://openalex.org/W3196044848","https://openalex.org/W3211853974","https://openalex.org/W4211189626"],"related_works":["https://openalex.org/W58658798","https://openalex.org/W3114375939","https://openalex.org/W3008821054","https://openalex.org/W2759696718","https://openalex.org/W2359816675","https://openalex.org/W2000379092","https://openalex.org/W2153096481","https://openalex.org/W2148616436","https://openalex.org/W2152497502","https://openalex.org/W2102525122"],"abstract_inverted_index":{"Today,":[0],"various":[1],"domain-specific":[2],"convolutional":[3],"neural":[4],"network":[5],"(CNN)":[6],"accelerators":[7,32],"are":[8,33,64,70],"deployed":[9,49],"in":[10,50],"large-scale":[11],"systems":[12],"to":[13,36,56,92],"satisfy":[14],"the":[15,28,60,66,74],"massive":[16],"computational":[17],"demands":[18],"of":[19,76,109],"current":[20],"deep":[21],"CNNs.":[22],"Although":[23,59],"bringing":[24],"significant":[25,142],"performance":[26,143],"improvements,":[27],"highly":[29],"integrated":[30],"CNN":[31,77,99],"more":[34,54],"susceptible":[35],"faults":[37],"caused":[38],"by":[39],"radiation,":[40],"aging,":[41],"and":[42,118,130,137],"process":[43],"variation.":[44],"CNNs":[45],"have":[46],"been":[47],"increasingly":[48],"security-critical":[51],"areas,":[52],"requiring":[53],"attention":[55],"reliable":[57],"execution.":[58],"classical":[61],"fault-tolerant":[62],"approaches":[63],"error-effective,":[65],"performance/energy":[67],"overheads":[68],"introduced":[69],"nonnegligible,":[71],"which":[72],"is":[73],"opposite":[75],"accelerator":[78,100],"design":[79],"philosophy.":[80],"In":[81],"this":[82],"article,":[83],"we":[84,105],"leverage":[85],"CNN\u2019s":[86],"intrinsic":[87],"tolerance":[88,116,123],"for":[89,98],"minor":[90],"errors":[91],"explore":[93],"approximate":[94,114,121],"fault-tolerance":[95,101],"(ApFT)":[96],"opportunities":[97],"overhead":[102],"reduction.":[103],"Specifically,":[104],"discuss":[106],"two":[107],"branches":[108],"ApFT":[110],"designs:":[111],"selective":[112],"duplicating-based":[113],"fault":[115,122],"(S-ApFT)":[117],"imprecise":[119],"checking-based":[120],"(I-ApFT).":[124],"The":[125],"results":[126],"show":[127],"that":[128],"S-ApFT":[129],"I-ApFT":[131],"can":[132],"achieve":[133],"comparable":[134],"error-detection":[135],"ability":[136],"dual-modular":[138],"redundancy":[139],"while":[140],"achieving":[141],"improvements.":[144]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-18T07:39:51.176621","created_date":"2025-10-10T00:00:00"}
