{"id":"https://openalex.org/W4386859267","doi":"https://doi.org/10.1109/islped58423.2023.10244486","title":"Uncertainty-Aware Online Learning for Dynamic Power Management in Large Manycore Systems","display_name":"Uncertainty-Aware Online Learning for Dynamic Power Management in Large Manycore Systems","publication_year":2023,"publication_date":"2023-08-07","ids":{"openalex":"https://openalex.org/W4386859267","doi":"https://doi.org/10.1109/islped58423.2023.10244486"},"language":"en","primary_location":{"id":"doi:10.1109/islped58423.2023.10244486","is_oa":false,"landing_page_url":"https://doi.org/10.1109/islped58423.2023.10244486","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)","raw_type":"proceedings-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/A5019597041","display_name":"Gaurav Narang","orcid":"https://orcid.org/0000-0001-9517-1280"},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Gaurav Narang","raw_affiliation_strings":["School of EECS, Washington State University,Pullman,WA,USA","School of EECS, Washington State University, Pullman, WA, USA"],"affiliations":[{"raw_affiliation_string":"School of EECS, Washington State University,Pullman,WA,USA","institution_ids":["https://openalex.org/I72951846"]},{"raw_affiliation_string":"School of EECS, Washington State University, Pullman, WA, USA","institution_ids":["https://openalex.org/I72951846"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078114699","display_name":"Raid Ayoub","orcid":"https://orcid.org/0000-0002-1175-2983"},"institutions":[{"id":"https://openalex.org/I1343180700","display_name":"Intel (United States)","ror":"https://ror.org/01ek73717","country_code":"US","type":"company","lineage":["https://openalex.org/I1343180700"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Raid Ayoub","raw_affiliation_strings":["Intel Corporation,Hillsboro,OR,USA","Intel Corporation, Hillsboro, OR, USA"],"affiliations":[{"raw_affiliation_string":"Intel Corporation,Hillsboro,OR,USA","institution_ids":["https://openalex.org/I1343180700"]},{"raw_affiliation_string":"Intel Corporation, Hillsboro, OR, USA","institution_ids":["https://openalex.org/I1343180700"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007256099","display_name":"Michael Kishinevsky","orcid":"https://orcid.org/0000-0002-5593-9694"},"institutions":[{"id":"https://openalex.org/I1343180700","display_name":"Intel (United States)","ror":"https://ror.org/01ek73717","country_code":"US","type":"company","lineage":["https://openalex.org/I1343180700"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael Kishinevsky","raw_affiliation_strings":["Intel Corporation,Hillsboro,OR,USA","Intel Corporation, Hillsboro, OR, USA"],"affiliations":[{"raw_affiliation_string":"Intel Corporation,Hillsboro,OR,USA","institution_ids":["https://openalex.org/I1343180700"]},{"raw_affiliation_string":"Intel Corporation, Hillsboro, OR, USA","institution_ids":["https://openalex.org/I1343180700"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055445718","display_name":"Janardhan Rao Doppa","orcid":"https://orcid.org/0000-0002-3848-5301"},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Janardhan Rao Doppa","raw_affiliation_strings":["School of EECS, Washington State University,Pullman,WA,USA","School of EECS, Washington State University, Pullman, WA, USA"],"affiliations":[{"raw_affiliation_string":"School of EECS, Washington State University,Pullman,WA,USA","institution_ids":["https://openalex.org/I72951846"]},{"raw_affiliation_string":"School of EECS, Washington State University, Pullman, WA, USA","institution_ids":["https://openalex.org/I72951846"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078441163","display_name":"Partha Pratim Pande","orcid":"https://orcid.org/0000-0002-5930-8531"},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Partha Pratim Pande","raw_affiliation_strings":["School of EECS, Washington State University,Pullman,WA,USA","School of EECS, Washington State University, Pullman, WA, USA"],"affiliations":[{"raw_affiliation_string":"School of EECS, Washington State University,Pullman,WA,USA","institution_ids":["https://openalex.org/I72951846"]},{"raw_affiliation_string":"School of EECS, Washington State University, Pullman, WA, USA","institution_ids":["https://openalex.org/I72951846"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5019597041"],"corresponding_institution_ids":["https://openalex.org/I72951846"],"apc_list":null,"apc_paid":null,"fwci":0.2678,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.54158821,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10363","display_name":"Low-power high-performance VLSI design","score":0.9998000264167786,"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/T10363","display_name":"Low-power high-performance VLSI design","score":0.9998000264167786,"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/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.9997000098228455,"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/T10829","display_name":"Interconnection Networks and Systems","score":0.9994000196456909,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8099216222763062},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.681874692440033},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4817911982536316},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44526875019073486},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4239342510700226},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36096692085266113}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8099216222763062},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.681874692440033},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4817911982536316},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44526875019073486},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4239342510700226},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36096692085266113},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/islped58423.2023.10244486","is_oa":false,"landing_page_url":"https://doi.org/10.1109/islped58423.2023.10244486","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G3521142995","display_name":null,"funder_award_id":"CNS-1955353,OAC-1910213","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2028800864","https://openalex.org/W2038229768","https://openalex.org/W2101234009","https://openalex.org/W2135370784","https://openalex.org/W2145021036","https://openalex.org/W2145339207","https://openalex.org/W2147657366","https://openalex.org/W2170382128","https://openalex.org/W2343995837","https://openalex.org/W2471138382","https://openalex.org/W2521200999","https://openalex.org/W2619632555","https://openalex.org/W2736601468","https://openalex.org/W2898122376","https://openalex.org/W2964578286","https://openalex.org/W3026208461","https://openalex.org/W3089746993","https://openalex.org/W3124964988","https://openalex.org/W3180462067","https://openalex.org/W4280503416","https://openalex.org/W4280565028","https://openalex.org/W4286793170","https://openalex.org/W4287075774","https://openalex.org/W4293713156","https://openalex.org/W6741002519"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Large-scale":[0],"manycore":[1,35],"System-on-Chips":[2],"(SoCs)":[3],"need":[4],"to":[5,84,92,110,123,143,149,175,183],"satisfy":[6],"the":[7,26,53,66,86,136,145,151,169],"conflicting":[8],"objectives":[9],"of":[10,28,60,65],"maximizing":[11],"performance":[12],"and":[13,114,155,178,187],"minimizing":[14],"energy":[15],"consumption":[16],"for":[17,37,57,128,159],"dynamically":[18],"changing":[19],"applications.":[20],"In":[21],"this":[22],"paper,":[23],"we":[24,119],"consider":[25],"problem":[27],"dynamic":[29],"power":[30],"management":[31],"(DPM)":[32],"in":[33],"large":[34],"SoCs":[36],"unseen":[38,93,115,129],"applications":[39,94],"at":[40,95],"runtime.":[41,96],"We":[42,76,132],"employ":[43,120],"a":[44,63,78],"machine":[45],"learning":[46,82,186,189],"(ML)":[47],"based":[48],"DPM":[49,87],"policy,":[50,88],"which":[51,89],"selects":[52],"voltage/frequency":[54],"(V/F)":[55],"levels":[56],"different":[58],"cluster":[59],"cores":[61],"as":[62,70],"function":[64],"application":[67],"features":[68],"such":[69],"core":[71],"computation,":[72],"inter-core":[73],"traffic":[74],"etc.":[75],"propose":[77],"novel":[79],"uncertainty-aware":[80],"online":[81,160],"framework":[83],"learn":[85],"can":[90],"adapt":[91],"It":[97],"relies":[98],"on":[99,164],"two":[100],"key":[101],"ideas.":[102],"First,":[103],"an":[104],"entropy-based":[105],"uncertainty":[106],"measure":[107],"is":[108,171],"used":[109],"distinguish":[111],"between":[112],"seen":[113],"system":[116,130,166],"states.":[117,131],"Second,":[118],"conformal":[121],"prediction":[122],"compute":[124],"uncertain":[125,137],"V/F":[126,138,147],"sets":[127],"perform":[133],"bounded-search":[134],"over":[135],"configurations":[139,148],"using":[140],"power/performance":[141],"models":[142],"identify":[144],"best":[146],"minimize":[150],"energy-delay":[152],"product":[153],"(EDP)":[154],"create":[156],"supervised":[157],"examples":[158],"learning.":[161],"Our":[162],"experiments":[163],"64-core":[165],"show":[167],"that":[168],"EDP":[170],"reduced":[172],"by":[173],"up":[174],"50":[176],"%":[177,180],"60":[179],"when":[181],"compared":[182],"existing":[184],"online-imitation":[185],"reinforcement":[188],"methods,":[190],"respectively.":[191]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}