{"id":"https://openalex.org/W4310887948","doi":"https://doi.org/10.1145/3570604","title":"Characterizing the Performance of Accelerated Jetson Edge Devices for Training Deep Learning Models","display_name":"Characterizing the Performance of Accelerated Jetson Edge Devices for Training Deep Learning Models","publication_year":2022,"publication_date":"2022-12-01","ids":{"openalex":"https://openalex.org/W4310887948","doi":"https://doi.org/10.1145/3570604"},"language":"en","primary_location":{"id":"doi:10.1145/3570604","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3570604","pdf_url":null,"source":{"id":"https://openalex.org/S4210193547","display_name":"Proceedings of the ACM on Measurement and Analysis of Computing Systems","issn_l":"2476-1249","issn":["2476-1249"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Measurement and Analysis of Computing Systems","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2509.20160","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082955483","display_name":"Prashanthi S.K","orcid":null},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Prashanthi S.K","raw_affiliation_strings":["Indian Institute of Science, Bangalore, India"],"raw_orcid":"https://orcid.org/0000-0002-7490-3128","affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore, India","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079921074","display_name":"Sai Anuroop Kesanapalli","orcid":"https://orcid.org/0000-0003-1270-7460"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sai Anuroop Kesanapalli","raw_affiliation_strings":["Indian Institute of Science, Bangalore, India"],"raw_orcid":"https://orcid.org/0000-0003-1270-7460","affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore, India","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041794289","display_name":"Yogesh Simmhan","orcid":"https://orcid.org/0000-0003-4140-7774"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Yogesh Simmhan","raw_affiliation_strings":["Indian Institute of Science, Bangalore, India"],"raw_orcid":"https://orcid.org/0000-0003-4140-7774","affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore, India","institution_ids":["https://openalex.org/I59270414"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I59270414"],"apc_list":null,"apc_paid":null,"fwci":2.7991,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.92148521,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"6","issue":"3","first_page":"1","last_page":"26"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994000196456909,"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":0.9994000196456909,"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/T13553","display_name":"Age of Information Optimization","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9980999827384949,"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":0.7948517203330994},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.7110536694526672},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.6297472715377808},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.5840281248092651},{"id":"https://openalex.org/keywords/edge-computing","display_name":"Edge computing","score":0.5350291728973389},{"id":"https://openalex.org/keywords/workstation","display_name":"Workstation","score":0.4981858730316162},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45365798473358154},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4413694143295288},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4213646352291107},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.41701990365982056},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.39989426732063293},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.3803102374076843},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3622354567050934},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.35753530263900757},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.1820460557937622},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.1479291021823883}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7948517203330994},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.7110536694526672},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.6297472715377808},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.5840281248092651},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.5350291728973389},{"id":"https://openalex.org/C67953723","wikidata":"https://www.wikidata.org/wiki/Q192525","display_name":"Workstation","level":2,"score":0.4981858730316162},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45365798473358154},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4413694143295288},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4213646352291107},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.41701990365982056},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.39989426732063293},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.3803102374076843},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3622354567050934},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.35753530263900757},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.1820460557937622},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.1479291021823883},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3570604","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3570604","pdf_url":null,"source":{"id":"https://openalex.org/S4210193547","display_name":"Proceedings of the ACM on Measurement and Analysis of Computing Systems","issn_l":"2476-1249","issn":["2476-1249"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Measurement and Analysis of Computing Systems","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2509.20160","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.20160","pdf_url":"https://arxiv.org/pdf/2509.20160","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai::80120","is_oa":false,"landing_page_url":"https://hdl.handle.net/20.500.12733/1581742","pdf_url":null,"source":{"id":"https://openalex.org/S4306402641","display_name":"LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Cient\u00edficas)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4383465926","host_organization_name":"LA Referencia","host_organization_lineage":["https://openalex.org/I4383465926"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2509.20160","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.20160","pdf_url":"https://arxiv.org/pdf/2509.20160","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320719","display_name":"Department of Science and Technology, Ministry of Science and Technology, India","ror":"https://ror.org/0101xrq71"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2194775991","https://openalex.org/W2501610572","https://openalex.org/W2734941459","https://openalex.org/W2788715907","https://openalex.org/W2943201207","https://openalex.org/W2950865323","https://openalex.org/W2960833983","https://openalex.org/W2962835968","https://openalex.org/W2964787841","https://openalex.org/W2982083293","https://openalex.org/W3004066795","https://openalex.org/W3035305184","https://openalex.org/W3125157485","https://openalex.org/W3133174490","https://openalex.org/W3138303811","https://openalex.org/W3154065259","https://openalex.org/W4240768087","https://openalex.org/W4287990567"],"related_works":["https://openalex.org/W4322761281","https://openalex.org/W4238233472","https://openalex.org/W4312996489","https://openalex.org/W3111395152","https://openalex.org/W4313526662","https://openalex.org/W3106131444","https://openalex.org/W3216099748","https://openalex.org/W4205963435","https://openalex.org/W4313463379","https://openalex.org/W3214037210"],"abstract_inverted_index":{"Deep":[0],"Neural":[1],"Networks":[2],"(DNNs)":[3],"have":[4,84],"had":[5],"a":[6,57,104,210,230],"significant":[7],"impact":[8],"on":[9,20,32,63,110,154,191,199],"domains":[10],"like":[11,39,50],"autonomous":[12],"vehicles":[13],"and":[14,42,70,78,90,97,123,134,141,147,150,156,165,173,196,202,238],"smart":[15],"cities":[16],"through":[17],"low-latency":[18],"inferencing":[19],"edge":[21,34,48,207],"computing":[22],"devices":[23,49,82,112],"close":[24],"to":[25,88,217,228,233],"the":[26,33,43,54,64,188,192,235],"data":[27],"source.":[28],"However,":[29],"DNN":[30,61,108,128,211,245],"training":[31,62,109,135,161,189,236],"is":[35,68],"poorly":[36],"explored.":[37],"Techniques":[38],"federated":[40,218],"learning":[41,219],"growing":[44],"capacity":[45],"of":[46,60,107,113],"GPU-accelerated":[47],"NVIDIA":[51],"Jetson":[52,116],"motivate":[53],"need":[55],"for":[56,125,209,242],"holistic":[58],"characterization":[59],"edge.":[65],"Training":[66],"DNNs":[67],"resource-intensive":[69],"can":[71,185],"stress":[72],"an":[73,205,222],"edge's":[74],"GPU,":[75],"CPU,":[76],"memory":[77,96],"storage":[79,99,143],"capacities.":[80],"Edge":[81],"also":[83,177],"different":[85,247],"resources":[86],"compared":[87],"workstations":[89],"servers,":[91],"such":[92,137],"as":[93,138],"slower":[94],"shared":[95],"diverse":[98,127],"media.":[100],"Here,":[101],"we":[102,224],"perform":[103],"principled":[105],"study":[106,184],"individual":[111],"three":[114,126],"contemporary":[115],"device":[117,133],"types:":[118],"AGX":[119],"Xavier,":[120],"Xavier":[121],"NX":[122],"Nano":[124],"model--dataset":[129],"combinations.":[130],"We":[131],"vary":[132],"parameters":[136],"I/O":[139],"pipelining":[140],"parallelism,":[142],"media,":[144],"mini-batch":[145],"sizes":[146],"power":[148,248],"modes,":[149,249],"examine":[151],"their":[152],"effect":[153],"CPU":[155],"GPU":[157],"utilization,":[158],"fetch":[159],"stalls,":[160],"time,":[162],"energy":[163,197,239],"usage,":[164],"variability.":[166],"Our":[167,182],"analysis":[168],"exposes":[169],"several":[170],"resource":[171],"inter-dependencies":[172],"counter-intuitive":[174],"insights,":[175],"while":[176],"helping":[178],"quantify":[179],"known":[180],"wisdom.":[181],"rigorous":[183],"help":[186],"tune":[187],"performance":[190],"edge,":[193],"trade-off":[194],"time":[195,237],"usage":[198],"constrained":[200],"devices,":[201],"even":[203],"select":[204],"ideal":[206],"hardware":[208],"workload,":[212],"and,":[213],"in":[214],"future,":[215],"extend":[216],"too.":[220],"As":[221],"illustration,":[223],"use":[225],"these":[226],"results":[227],"build":[229],"simple":[231],"model":[232],"predict":[234],"per":[240],"epoch":[241],"any":[243],"given":[244],"across":[246],"with":[250],"minimal":[251],"additional":[252],"profiling.":[253]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":10}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2022-12-20T00:00:00"}