乐胖代购免代理版

{"id":"https://openalex.org/W2945387900","doi":"https://doi.org/10.1145/3316781.3317742","title":"A Fault-Tolerant Neural Network Architecture","display_name":"A Fault-Tolerant Neural Network Architecture","publication_year":2019,"publication_date":"2019-05-23","ids":{"openalex":"https://openalex.org/W2945387900","doi":"https://doi.org/10.1145/3316781.3317742","mag":"2945387900"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3316781.3317742","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5071128160","display_name":"Tao Liu","orcid":"https://orcid.org/0000-0002-5212-2806"},"institutions":[{"id":"https://openalex.org/I19700959","display_name":"Florida International University","ror":"https://ror.org/02gz6gg07","country_code":"US","type":"education","lineage":["https://openalex.org/I19700959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tao Liu","raw_affiliation_strings":["Florida International University"],"affiliations":[{"raw_affiliation_string":"Florida International University","institution_ids":["https://openalex.org/I19700959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067226050","display_name":"Wujie Wen","orcid":"https://orcid.org/0000-0003-0011-0675"},"institutions":[{"id":"https://openalex.org/I19700959","display_name":"Florida International University","ror":"https://ror.org/02gz6gg07","country_code":"US","type":"education","lineage":["https://openalex.org/I19700959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wujie Wen","raw_affiliation_strings":["Florida International University"],"affiliations":[{"raw_affiliation_string":"Florida International University","institution_ids":["https://openalex.org/I19700959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103098320","display_name":"Lei Jiang","orcid":"https://orcid.org/0000-0003-4795-0284"},"institutions":[{"id":"https://openalex.org/I4210119109","display_name":"Indiana University Bloomington","ror":"https://ror.org/02k40bc56","country_code":"US","type":"education","lineage":["https://openalex.org/I4210119109","https://openalex.org/I592451"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lei Jiang","raw_affiliation_strings":["Indiana University Bloomington"],"affiliations":[{"raw_affiliation_string":"Indiana University Bloomington","institution_ids":["https://openalex.org/I4210119109"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100651384","display_name":"Yanzhi Wang","orcid":"https://orcid.org/0000-0002-3024-7990"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Yanzhi Wang","raw_affiliation_strings":["Northeastern University"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016268650","display_name":"Chengmo Yang","orcid":"https://orcid.org/0000-0003-0978-1504"},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chengmo Yang","raw_affiliation_strings":["University of Delaware"],"affiliations":[{"raw_affiliation_string":"University of Delaware","institution_ids":["https://openalex.org/I86501945"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018684814","display_name":"Gang Quan","orcid":"https://orcid.org/0000-0002-1007-4850"},"institutions":[{"id":"https://openalex.org/I19700959","display_name":"Florida International University","ror":"https://ror.org/02gz6gg07","country_code":"US","type":"education","lineage":["https://openalex.org/I19700959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gang Quan","raw_affiliation_strings":["Florida International University"],"affiliations":[{"raw_affiliation_string":"Florida International University","institution_ids":["https://openalex.org/I19700959"]}]}],"institution_assertions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":11.283,"has_fulltext":false,"cited_by_count":48,"citation_normalized_percentile":{"value":0.999954,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Memristive Devices for Neuromorphic Computing","score":1.0,"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/T10502","display_name":"Memristive Devices for Neuromorphic Computing","score":1.0,"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/T12808","display_name":"Ferroelectric Devices for Low-Power Nanoscale Applications","score":0.9995,"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/T10036","display_name":"Deep Learning in Computer Vision and Image Recognition","score":0.9987,"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/neural-network-architectures","display_name":"Neural Network Architectures","score":0.552302},{"id":"https://openalex.org/keywords/resistive-switching","display_name":"Resistive Switching","score":0.535143}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7877826},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5968114},{"id":"https://openalex.org/C63540848","wikidata":"https://www.wikidata.org/wiki/Q3140932","display_name":"Fault tolerance","level":2,"score":0.59062886},{"id":"https://openalex.org/C182019814","wikidata":"https://www.wikidata.org/wiki/Q1143830","display_name":"Resistive random-access memory","level":3,"score":0.54395175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4541459},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42546582},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.42400855},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.34336025},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2507248},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10015488},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3316781.3317742","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.56,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":26,"referenced_works":["https://openalex.org/W1595781024","https://openalex.org/W1676820704","https://openalex.org/W1797153894","https://openalex.org/W2025636917","https://openalex.org/W2033811947","https://openalex.org/W2108598243","https://openalex.org/W2114766824","https://openalex.org/W2149933564","https://openalex.org/W2156163116","https://openalex.org/W2257979135","https://openalex.org/W2279098554","https://openalex.org/W2317230785","https://openalex.org/W2518281301","https://openalex.org/W2540279855","https://openalex.org/W2612375349","https://openalex.org/W2613989746","https://openalex.org/W2625840880","https://openalex.org/W2774575696","https://openalex.org/W2795127895","https://openalex.org/W2962717913","https://openalex.org/W2963186210","https://openalex.org/W2964299589","https://openalex.org/W2991309320","https://openalex.org/W3099743262","https://openalex.org/W4243519499","https://openalex.org/W752222625"],"related_works":["https://openalex.org/W4396815615","https://openalex.org/W4381388454","https://openalex.org/W3161624601","https://openalex.org/W2952918855","https://openalex.org/W2545245183","https://openalex.org/W2350916061","https://openalex.org/W2078381924","https://openalex.org/W2054635671","https://openalex.org/W2017425642","https://openalex.org/W1970117475"],"abstract_inverted_index":{"New":[0],"DNN":[1,50,84,102,127,150],"accelerators":[2,151],"based":[3],"on":[4,125],"emerging":[5],"technologies,":[6],"such":[7],"as":[8],"resistive":[9],"random":[10],"access":[11],"memory":[12,41],"(ReRAM),":[13],"are":[14,31],"gaining":[15],"increasing":[16],"research":[17],"attention":[18],"given":[19],"their":[20],"potential":[21],"of":[22,49,121,163],"\"in-situ\"":[23],"data":[24],"processing.":[25],"Unfortunately,":[26],"device-level":[27],"physical":[28],"limitations":[29],"that":[30,133],"unique":[32],"to":[33,63,81,113],"these":[34],"technologies":[35],"may":[36],"cause":[37],"weight":[38,66,147],"disturbance":[39,67,148],"in":[40,160],"and":[42,47,100,129],"thus":[43,155],"compromising":[44],"the":[45,65,83,88,116,134,143],"performance":[46],"stability":[48,85],"accelerators.":[51],"In":[52],"this":[53],"work,":[54],"we":[55,74],"propose":[56,75],"a":[57,76,161],"novel":[58,77],"fault-tolerant":[59,136],"neural":[60,137],"network":[61,138],"architecture":[62,139],"mitigate":[64],"problem":[68],"without":[69],"involving":[70],"expensive":[71],"retraining.":[72],"Specifically,":[73],"collaborative":[78],"logistic":[79],"classifier":[80],"enhance":[82],"by":[86],"redesigning":[87],"binary":[89],"classifiers":[90],"augmented":[91],"from":[92],"both":[93],"traditional":[94],"error":[95],"correction":[96],"output":[97],"code":[98],"(ECOC)":[99],"modern":[101],"training":[103],"algorithm.":[104],"We":[105],"also":[106],"develop":[107],"an":[108],"optimized":[109],"variable-length":[110],"\"decode-free\"":[111],"scheme":[112],"further":[114],"boost":[115],"accuracy":[117,144],"under":[118],"fewer":[119],"number":[120],"classifiers.":[122],"Experimental":[123],"results":[124],"cutting-edge":[126],"models":[128],"complex":[130],"datasets":[131],"show":[132],"proposed":[135],"can":[140],"effectively":[141],"rectify":[142],"degradation":[145],"against":[146],"for":[149,157],"with":[152],"low":[153],"cost,":[154],"allowing":[156],"its":[158],"deployment":[159],"variety":[162],"mainstream":[164],"DNNs.":[165]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2945387900","counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":2}],"updated_date":"2024-11-27T13:22:58.100988","created_date":"2019-05-29"}