乐胖代购免代理版

{"id":"https://openalex.org/W4399120233","doi":"https://doi.org/10.1109/vts60656.2024.10538510","title":"Analyzing and Mitigating Circuit Aging Effects in Deep Learning Accelerators","display_name":"Analyzing and Mitigating Circuit Aging Effects in Deep Learning Accelerators","publication_year":2024,"publication_date":"2024-04-22","ids":{"openalex":"https://openalex.org/W4399120233","doi":"https://doi.org/10.1109/vts60656.2024.10538510"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/vts60656.2024.10538510","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/A5109719660","display_name":"Sanjay Das","orcid":null},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sanjay Das","raw_affiliation_strings":["University of Texas at Dallas, TX, USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at Dallas, TX, USA","institution_ids":["https://openalex.org/I162577319"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066094401","display_name":"Shamik Kundu","orcid":"https://orcid.org/0000-0002-5992-8554"},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shamik Kundu","raw_affiliation_strings":["University of Texas at Dallas, TX, USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at Dallas, TX, USA","institution_ids":["https://openalex.org/I162577319"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113232101","display_name":"Anand Menon","orcid":null},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anand Menon","raw_affiliation_strings":["University of Texas at Dallas, TX, USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at Dallas, TX, USA","institution_ids":["https://openalex.org/I162577319"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013719267","display_name":"Yihui Ren","orcid":"https://orcid.org/0000-0002-5750-6964"},"institutions":[{"id":"https://openalex.org/I200870766","display_name":"Brookhaven National Laboratory","ror":"https://ror.org/02ex6cf31","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I200870766","https://openalex.org/I39565521","https://openalex.org/I4210142672"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yihui Ren","raw_affiliation_strings":["Brookhaven National Laboratory, NY, USA"],"affiliations":[{"raw_affiliation_string":"Brookhaven National Laboratory, NY, USA","institution_ids":["https://openalex.org/I200870766"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5098923015","display_name":"Shubha Kharel","orcid":null},"institutions":[{"id":"https://openalex.org/I200870766","display_name":"Brookhaven National Laboratory","ror":"https://ror.org/02ex6cf31","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I200870766","https://openalex.org/I39565521","https://openalex.org/I4210142672"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shubha Kharel","raw_affiliation_strings":["Brookhaven National Laboratory, NY, USA"],"affiliations":[{"raw_affiliation_string":"Brookhaven National Laboratory, NY, USA","institution_ids":["https://openalex.org/I200870766"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066320524","display_name":"Kanad Basu","orcid":"https://orcid.org/0000-0002-6431-7512"},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kanad Basu","raw_affiliation_strings":["University of Texas at Dallas, TX, USA"],"affiliations":[{"raw_affiliation_string":"University of Texas at Dallas, TX, USA","institution_ids":["https://openalex.org/I162577319"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":84},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9989,"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":"Advanced Memory and Neural Computing","score":0.9989,"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/T11005","display_name":"Radiation Effects in Electronics","score":0.9986,"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 and Negative Capacitance Devices","score":0.9966,"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/deep-neural-networks","display_name":"Deep Neural Networks","score":0.4116515}],"concepts":[{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.67026246},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6581234},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.54867965},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45806152},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.4445464},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4428833},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4116515},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.36226296},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28220275},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15636349},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.12354475},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/vts60656.2024.10538510","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":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":14,"referenced_works":["https://openalex.org/W2071543005","https://openalex.org/W2107562186","https://openalex.org/W2108957996","https://openalex.org/W2146787584","https://openalex.org/W2154451732","https://openalex.org/W2171904653","https://openalex.org/W2942566099","https://openalex.org/W3120770213","https://openalex.org/W3131039502","https://openalex.org/W3144243563","https://openalex.org/W3147015593","https://openalex.org/W3184114148","https://openalex.org/W4385453267","https://openalex.org/W4391382408"],"related_works":["https://openalex.org/W986318368","https://openalex.org/W4377865163","https://openalex.org/W4315865067","https://openalex.org/W3208304128","https://openalex.org/W3193857078","https://openalex.org/W3000197790","https://openalex.org/W2979433843","https://openalex.org/W2888956734","https://openalex.org/W2384410913","https://openalex.org/W2000785801"],"abstract_inverted_index":{"The":[0,213],"widespread":[1],"adoption":[2],"of":[3,41,56,63,69,76,101,107,126,134,146,154,194,205,252],"Deep":[4],"Neural":[5],"Networks":[6],"(DNNs)":[7],"can":[8],"be":[9],"attributed":[10],"to":[11,60,72,84,118,139,149,159,196,209,224,232],"their":[12],"remarkable":[13],"performance":[14,106,173,192],"in":[15,26,32,44,202,228],"tackling":[16],"complex":[17],"real-world":[18],"problems.":[19],"Consequently,":[20],"they":[21],"have":[22],"found":[23],"extensive":[24,183],"use":[25],"everyday":[27],"applications":[28,109],"as":[29,31,157,207,220],"well":[30],"high-assurance":[33],"environments.":[34],"Nonetheless,":[35],"various":[36],"challenges":[37],"undermine":[38],"the":[39,61,74,80,98,105,124,132,144,151,155,203,233,250,253],"reliability":[40],"these":[42,90],"DNNs":[43],"mission-critical":[45],"scenarios.":[46],"One":[47],"such":[48,120],"challenge":[49],"is":[50,68,131,240],"circuit":[51,64,77,102],"aging,":[52,206],"an":[53,245],"inevitable":[54],"consequence":[55],"prolonged":[57],"usage":[58],"leading":[59],"deterioration":[62],"performance.":[65],"Therefore,":[66],"it":[67],"utmost":[70],"importance":[71],"grasp":[73],"implications":[75],"aging":[78,103,177],"at":[79],"application":[81,235],"level":[82],"and":[83,110],"adopt":[85],"proactive":[86],"strategies":[87],"for":[88],"mitigating":[89],"effects.":[91],"Towards":[92],"this":[93,129,238],"end,":[94],"our":[95,127],"paper":[96],"examines":[97],"adverse":[99],"effects":[100],"on":[104,171],"DNN":[108,187],"introduce":[111],"a":[112,165,172,179],"novel":[113],"aging-aware":[114],"training":[115,210],"(AAT)":[116],"framework":[117,130],"mitigate":[119],"detrimental":[121],"impacts.":[122],"To":[123],"best":[125],"knowledge,":[128],"first":[133],"its":[135,160],"kind,":[136],"expressly":[137],"tailored":[138],"train":[140],"models":[141],"while":[142],"considering":[143],"impact":[145],"aging.":[147],"Additionally,":[148],"extend":[150],"operational":[152],"lifespan":[153],"system,":[156],"opposed":[158],"immediate":[161],"disposal,":[162],"we":[163,189],"advocate":[164],"strategic":[166],"model":[167,214],"replacement":[168,215],"approach":[169,216],"based":[170],"threshold,":[174],"particularly":[175],"when":[176,198,230],"becomes":[178],"prominent":[180],"concern.":[181],"Through":[182],"experiments":[184],"involving":[185],"cutting-edge":[186],"models,":[188],"observe":[190],"substantial":[191],"enhancements":[193],"up":[195,223],"78%":[197],"utilizing":[199],"AAT,":[200,243],"even":[201],"presence":[204],"compared":[208],"without":[211],"AAT.":[212],"yields":[217],"significant":[218],"results":[219],"well,":[221],"exhibiting":[222],"30%":[225],"relative":[226],"improvement":[227,239],"accuracy":[229],"subjected":[231],"same":[234],"workload.":[236],"Furthermore,":[237],"augmented":[241],"with":[242],"achieving":[244],"additional":[246],"20%":[247],"improvement,":[248],"demonstrating":[249],"efficacy":[251],"proposed":[254],"framework.":[255]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4399120233","counts_by_year":[],"updated_date":"2024-12-13T17:13:00.044999","created_date":"2024-05-30"}