{"id":"https://openalex.org/W3110507830","doi":"https://doi.org/10.1145/3380446.3430640","title":"An Efficient and Flexible Learning Framework for Dynamic Power and Thermal Co-Management","display_name":"An Efficient and Flexible Learning Framework for Dynamic Power and Thermal Co-Management","publication_year":2020,"publication_date":"2020-11-16","ids":{"openalex":"https://openalex.org/W3110507830","doi":"https://doi.org/10.1145/3380446.3430640","mag":"3110507830"},"language":"en","primary_location":{"id":"doi:10.1145/3380446.3430640","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3380446.3430640","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD","raw_type":"proceedings-article"},"type":"conference-paper","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/A5066240426","display_name":"Yuan Cao","orcid":"https://orcid.org/0000-0002-7150-4682"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Cao","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101285252","display_name":"Tianhao Shen","orcid":null},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianhao Shen","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100425431","display_name":"Li Zhang","orcid":"https://orcid.org/0000-0001-7293-8119"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Zhang","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071383109","display_name":"Xunzhao Yin","orcid":"https://orcid.org/0000-0003-4656-9545"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xunzhao Yin","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054211420","display_name":"Cheng Zhuo","orcid":"https://orcid.org/0000-0002-2610-7522"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Zhuo","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":0.6762,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.64641049,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"117","last_page":"122"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T12238","display_name":"Green IT and Sustainability","score":0.9977999925613403,"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/T10363","display_name":"Low-power high-performance VLSI design","score":0.9934999942779541,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.9074413776397705},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8224712610244751},{"id":"https://openalex.org/keywords/frequency-scaling","display_name":"Frequency scaling","score":0.751123309135437},{"id":"https://openalex.org/keywords/power-management","display_name":"Power management","score":0.5914223194122314},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.49246343970298767},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46343785524368286},{"id":"https://openalex.org/keywords/battery","display_name":"Battery (electricity)","score":0.4400199055671692},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.3533133864402771}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.9074413776397705},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8224712610244751},{"id":"https://openalex.org/C157742956","wikidata":"https://www.wikidata.org/wiki/Q3237776","display_name":"Frequency scaling","level":3,"score":0.751123309135437},{"id":"https://openalex.org/C2778774385","wikidata":"https://www.wikidata.org/wiki/Q4437810","display_name":"Power management","level":3,"score":0.5914223194122314},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.49246343970298767},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46343785524368286},{"id":"https://openalex.org/C555008776","wikidata":"https://www.wikidata.org/wiki/Q267298","display_name":"Battery (electricity)","level":3,"score":0.4400199055671692},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3533133864402771},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3380446.3430640","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3380446.3430640","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.6200000047683716}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1983101679","https://openalex.org/W1994948399","https://openalex.org/W2021247827","https://openalex.org/W2036853599","https://openalex.org/W2117299787","https://openalex.org/W2118780933","https://openalex.org/W2129960401","https://openalex.org/W2132301251","https://openalex.org/W2133761364","https://openalex.org/W2141721356","https://openalex.org/W2145339207","https://openalex.org/W2147657366","https://openalex.org/W2152165066","https://openalex.org/W2155382325","https://openalex.org/W2170382128","https://openalex.org/W2170749044","https://openalex.org/W2536874909","https://openalex.org/W2588838797","https://openalex.org/W2746553466","https://openalex.org/W2770465907","https://openalex.org/W2799462322","https://openalex.org/W2889137449","https://openalex.org/W2903140524","https://openalex.org/W2905199518","https://openalex.org/W2939944393","https://openalex.org/W2945804086","https://openalex.org/W2951693483","https://openalex.org/W2965301113","https://openalex.org/W2990628747","https://openalex.org/W2991403335","https://openalex.org/W2992514052","https://openalex.org/W2997796828","https://openalex.org/W3036319002","https://openalex.org/W3143296339"],"related_works":["https://openalex.org/W1967088250","https://openalex.org/W2144034023","https://openalex.org/W4245552802","https://openalex.org/W2135721350","https://openalex.org/W2036903086","https://openalex.org/W4287778142","https://openalex.org/W3025960525","https://openalex.org/W4238485050","https://openalex.org/W2134782496","https://openalex.org/W2055708881"],"abstract_inverted_index":{"At":[0],"the":[1,60,90,96,108,121],"era":[2],"of":[3,8],"Artificial":[4],"Intelligence":[5],"and":[6,26,49,71,76,94,100,112,119],"Internet":[7],"Things":[9],"(AIoT),":[10],"battery-powered":[11],"mobile":[12],"devices":[13],"are":[14],"required":[15],"to":[16],"perform":[17],"more":[18,31],"sophisticated":[19],"tasks":[20],"featured":[21],"with":[22,67,123,140],"fast":[23],"varying":[24],"workloads":[25,111],"constrained":[27],"power":[28,34,48,75],"supply,":[29],"demanding":[30],"efficient":[32,84],"run-time":[33,97],"management.":[35],"In":[36],"this":[37],"paper,":[38],"we":[39],"propose":[40],"a":[41],"deep":[42,85],"reinforcement":[43,86],"learning":[44,56,87,92],"framework":[45,133],"for":[46,63,74],"dynamic":[47,98],"thermal":[50],"co-management.":[51],"We":[52,80,114],"build":[53,82],"several":[54],"machine":[55,91],"models":[57,93],"that":[58,130],"incorporate":[59],"physical":[61],"details":[62],"an":[64,83],"ARM":[65],"Cortex-A72,":[66],"on":[68,107],"average":[69],"3%":[70],"1%":[72],"error":[73],"temperature":[77],"predictions,":[78],"respectively.":[79],"then":[81],"control":[88],"incorporating":[89],"facilitating":[95],"voltage":[99],"frequency":[101],"scaling":[102],"(DVFS)":[103],"strategy":[104],"selection":[105],"based":[106],"predicted":[109],"power,":[110],"temperature.":[113],"evaluate":[115],"our":[116,131],"proposed":[117,132],"framework,":[118],"compare":[120],"performance":[122,137],"existing":[124],"management":[125],"methods.":[126],"The":[127],"results":[128],"suggest":[129],"can":[134],"achieve":[135],"6.8%":[136],"improvement":[138],"compared":[139],"other":[141],"alternatives.":[142]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}