{"id":"https://openalex.org/W3216234944","doi":"https://doi.org/10.1109/les.2021.3129769","title":"Guaranteeing That Multilevel Prioritized DNN Models on an Embedded GPU Have Inference Performance Proportional to Respective Priorities","display_name":"Guaranteeing That Multilevel Prioritized DNN Models on an Embedded GPU Have Inference Performance Proportional to Respective Priorities","publication_year":2021,"publication_date":"2021-11-23","ids":{"openalex":"https://openalex.org/W3216234944","doi":"https://doi.org/10.1109/les.2021.3129769","mag":"3216234944"},"language":"en","primary_location":{"id":"doi:10.1109/les.2021.3129769","is_oa":false,"landing_page_url":"https://doi.org/10.1109/les.2021.3129769","pdf_url":null,"source":{"id":"https://openalex.org/S22443479","display_name":"IEEE Embedded Systems Letters","issn_l":"1943-0663","issn":["1943-0663","1943-0671"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Embedded Systems Letters","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/A5100638903","display_name":"Myungsun Kim","orcid":"https://orcid.org/0000-0003-4254-4009"},"institutions":[{"id":"https://openalex.org/I24214720","display_name":"Hansung University","ror":"https://ror.org/048m9x696","country_code":"KR","type":"education","lineage":["https://openalex.org/I24214720"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Myungsun Kim","raw_affiliation_strings":["Department of IT Convergence Engineering, Hansung University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-4254-4009","affiliations":[{"raw_affiliation_string":"Department of IT Convergence Engineering, Hansung University, Seoul, South Korea","institution_ids":["https://openalex.org/I24214720"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100638903"],"corresponding_institution_ids":["https://openalex.org/I24214720"],"apc_list":null,"apc_paid":null,"fwci":0.6796,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.72206693,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"14","issue":"2","first_page":"83","last_page":"86"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9983000159263611,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9979000091552734,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.9218239784240723},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.6411803960800171},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6113810539245605},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5512767434120178},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5285897254943848},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.4944019317626953},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.4574948251247406},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39718693494796753},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27253907918930054},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.21370676159858704}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.9218239784240723},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.6411803960800171},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6113810539245605},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5512767434120178},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5285897254943848},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.4944019317626953},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.4574948251247406},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39718693494796753},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27253907918930054},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.21370676159858704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/les.2021.3129769","is_oa":false,"landing_page_url":"https://doi.org/10.1109/les.2021.3129769","pdf_url":null,"source":{"id":"https://openalex.org/S22443479","display_name":"IEEE Embedded Systems Letters","issn_l":"1943-0663","issn":["1943-0663","1943-0671"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Embedded Systems Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321232","display_name":"Hansung University","ror":"https://ror.org/048m9x696"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1903969371","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2618530766","https://openalex.org/W2754430923","https://openalex.org/W2786171709","https://openalex.org/W2791175987","https://openalex.org/W2963446712","https://openalex.org/W6737664043"],"related_works":["https://openalex.org/W2341492732","https://openalex.org/W3187193180","https://openalex.org/W106542691","https://openalex.org/W1699080303","https://openalex.org/W4297799326","https://openalex.org/W3116064965","https://openalex.org/W4287027380","https://openalex.org/W3193760048","https://openalex.org/W3042419602","https://openalex.org/W2966649771"],"abstract_inverted_index":{"When":[0],"multiple":[1],"deep":[2],"neural":[3],"networks":[4],"(DNNs)":[5],"are":[6],"using":[7],"an":[8,12,109,112],"embedded":[9],"GPU":[10,79],"as":[11],"accelerator,":[13],"adjusting":[14],"the":[15,20,40,53,64,78,85,89,95,103,125,143],"CPU":[16],"occupancy":[17],"time":[18,97],"of":[19,67,98,127,133,138],"process":[21,114],"encompassing":[22],"each":[23,99],"DNN":[24,100,140],"on":[25],"a":[26,48,120],"priority":[27,35,38,69,86],"basis":[28],"does":[29],"not":[30],"always":[31],"guarantee":[32],"that":[33,50,106],"higher":[34,68],"DNNs":[36,70,75,118],"take":[37],"over":[39],"GPU.":[41,144],"To":[42],"address":[43],"this":[44],"problem,":[45],"we":[46],"propose":[47],"methodology":[49],"basically":[51],"uses":[52],"model":[54],"from":[55],"PyTorch":[56],"without":[57],"modification":[58],"while":[59],"providing":[60],"additional":[61],"advantages.":[62],"First,":[63],"response":[65],"performance":[66],"is":[71],"improved":[72],"by":[73,101],"allowing":[74],"to":[76,84,88,123],"occupy":[77],"in":[80,82,119],"preference":[81],"proportion":[83],"granted":[87],"hosting":[90],"processes.":[91],"Second,":[92],"it":[93],"reduces":[94],"execution":[96],"removing":[102],"multicontext":[104],"overhead":[105],"occurs":[107],"under":[108],"environment":[110],"with":[111],"independent":[113],"per":[115],"DNN,":[116],"executing":[117],"multithreaded":[121],"manner":[122],"overcome":[124],"limitation":[126],"pure":[128],"Python":[129],"and":[130],"taking":[131],"advantage":[132],"multistream":[134],"for":[135],"concurrent":[136],"running":[137],"different":[139],"operations":[141],"inside":[142]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
