{"id":"https://openalex.org/W4294068619","doi":"https://doi.org/10.1109/access.2022.3203401","title":"Deep Reinforcement Learning for System-on-Chip: Myths and Realities","display_name":"Deep Reinforcement Learning for System-on-Chip: Myths and Realities","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4294068619","doi":"https://doi.org/10.1109/access.2022.3203401"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3203401","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3203401","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2022.3203401","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058707991","display_name":"Tegg Taekyong Sung","orcid":"https://orcid.org/0000-0002-2967-4258"},"institutions":[{"id":"https://openalex.org/I4210149348","display_name":"EpiSys Science (United States)","ror":"https://ror.org/04k8txf58","country_code":"US","type":"company","lineage":["https://openalex.org/I4210149348"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tegg Taekyong Sung","raw_affiliation_strings":["EpiSys Science Inc., Poway, CA, USA"],"raw_orcid":"https://orcid.org/0000-0002-2967-4258","affiliations":[{"raw_affiliation_string":"EpiSys Science Inc., Poway, CA, USA","institution_ids":["https://openalex.org/I4210149348"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010757246","display_name":"Bo Ryu","orcid":"https://orcid.org/0000-0002-5447-5015"},"institutions":[{"id":"https://openalex.org/I4210149348","display_name":"EpiSys Science (United States)","ror":"https://ror.org/04k8txf58","country_code":"US","type":"company","lineage":["https://openalex.org/I4210149348"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bo Ryu","raw_affiliation_strings":["EpiSys Science Inc., Poway, CA, USA"],"raw_orcid":"https://orcid.org/0000-0002-5447-5015","affiliations":[{"raw_affiliation_string":"EpiSys Science Inc., Poway, CA, USA","institution_ids":["https://openalex.org/I4210149348"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210149348"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.6527,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.65031859,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"10","issue":null,"first_page":"98048","last_page":"98064"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.994700014591217,"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":0.994700014591217,"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/T10101","display_name":"Cloud Computing and Resource Management","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.9936000108718872,"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":0.7638111114501953},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6505897641181946},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6200041174888611},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.49369940161705017},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.48125407099723816},{"id":"https://openalex.org/keywords/randomness","display_name":"Randomness","score":0.47969481348991394},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44375744462013245},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4415234327316284},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.4398941993713379},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32814961671829224},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1361687183380127}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7638111114501953},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6505897641181946},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6200041174888611},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.49369940161705017},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.48125407099723816},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.47969481348991394},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44375744462013245},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4415234327316284},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.4398941993713379},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32814961671829224},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1361687183380127},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3203401","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3203401","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:890cb0132c634c7981c782a7585653f9","is_oa":true,"landing_page_url":"https://doaj.org/article/890cb0132c634c7981c782a7585653f9","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 98048-98064 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3203401","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3203401","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1958965543","https://openalex.org/W2025397485","https://openalex.org/W2094806030","https://openalex.org/W2105947650","https://openalex.org/W2170091282","https://openalex.org/W2173213060","https://openalex.org/W2335814492","https://openalex.org/W2495349330","https://openalex.org/W2546571074","https://openalex.org/W2749807327","https://openalex.org/W2912012512","https://openalex.org/W2968986602","https://openalex.org/W2980810797","https://openalex.org/W2982346115","https://openalex.org/W2984408162","https://openalex.org/W2998718698","https://openalex.org/W3020263945","https://openalex.org/W3022548332","https://openalex.org/W3033686353","https://openalex.org/W3042514743","https://openalex.org/W3098443589","https://openalex.org/W3109215866","https://openalex.org/W3136172274","https://openalex.org/W3157658131","https://openalex.org/W3171142476","https://openalex.org/W3198686482","https://openalex.org/W4205106731","https://openalex.org/W4213427189","https://openalex.org/W4214717370","https://openalex.org/W4223941569","https://openalex.org/W4225659972","https://openalex.org/W4287198310","https://openalex.org/W4288334231","https://openalex.org/W6631190155","https://openalex.org/W6638088447","https://openalex.org/W6683195989","https://openalex.org/W6683204974","https://openalex.org/W6690665300","https://openalex.org/W6713134421","https://openalex.org/W6736685754","https://openalex.org/W6739901393","https://openalex.org/W6743756900","https://openalex.org/W6756009870","https://openalex.org/W6759814162","https://openalex.org/W6761805307","https://openalex.org/W6763379649","https://openalex.org/W6769424276","https://openalex.org/W6774583691","https://openalex.org/W6782839094","https://openalex.org/W6794479900"],"related_works":["https://openalex.org/W3034924094","https://openalex.org/W3094954546","https://openalex.org/W1488708774","https://openalex.org/W1982811510","https://openalex.org/W4391100477","https://openalex.org/W4327779705","https://openalex.org/W1513698804","https://openalex.org/W4310560702","https://openalex.org/W2029712093","https://openalex.org/W4255503743"],"abstract_inverted_index":{"Neural":[0],"schedulers":[1,40,65,159],"based":[2],"on":[3],"deep":[4],"reinforcement":[5],"learning":[6],"(DRL)":[7],"have":[8,20],"shown":[9],"considerable":[10],"potential":[11],"for":[12,41,67,75,179],"solving":[13],"real-world":[14],"resource":[15,47],"allocation":[16,48],"problems,":[17],"as":[18,173,175],"they":[19],"demonstrated":[21],"significant":[22],"performance":[23,125,155],"gain":[24,126],"in":[25,93],"the":[26,36,42,107,113,120,124,128,136,139,147,154],"domain":[27,43,70],"of":[28,38,44,81,138,156],"cluster":[29,68],"computing.":[30],"In":[31],"this":[32,171],"paper,":[33],"we":[34,118,133],"investigate":[35],"feasibility":[37],"neural":[39,64,99,115,130,157,165],"System-on-Chip":[45],"(SoC)":[46],"through":[49],"extensive":[50],"experiments":[51],"and":[52,85,146],"comparison":[53],"with":[54,161],"non-neural,":[55],"heuristic":[56],"schedulers.":[57,116],"The":[58],"key":[59],"finding":[60],"is":[61],"three-fold.":[62],"First,":[63],"designed":[66],"computing":[69,83],"do":[71],"not":[72],"work":[73],"well":[74,174],"SoC":[76,82,158,166],"due":[77],"to":[78],"i)":[79],"heterogeneity":[80],"resources":[84],"ii)":[86],"variable":[87],"action":[88],"set":[89],"caused":[90],"by":[91,127],"randomness":[92],"incoming":[94],"jobs.":[95],"Second,":[96],"our":[97],"novel":[98],"scheduler":[100,167],"technique,":[101],"Eclectic":[102],"Interaction":[103],"Matching":[104],"(EIM),":[105],"overcomes":[106],"above":[108],"challenges,":[109],"thus":[110],"significantly":[111,152],"improving":[112],"existing":[114],"Specifically,":[117],"rationalize":[119],"underlying":[121],"reasons":[122],"behind":[123],"EIM-based":[129],"scheduler.":[131],"Third,":[132],"discover":[134],"that":[135],"ratio":[137],"average":[140,148],"processing":[141],"elements":[142],"(PE)":[143],"switching":[144],"delay":[145],"PE":[149],"computation":[150],"time":[151],"impacts":[153],"even":[160],"EIM.":[162],"Consequently,":[163],"future":[164],"design":[168],"must":[169],"consider":[170],"metric":[172],"its":[176],"implementation":[177],"overhead":[178],"practical":[180],"utility.":[181]},"counts_by_year":[{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2022-09-01T00:00:00"}
