{"id":"https://openalex.org/W4200256413","doi":"https://doi.org/10.1109/iccad51958.2021.9643589","title":"Optimizing VLSI Implementation with Reinforcement Learning - ICCAD Special Session Paper","display_name":"Optimizing VLSI Implementation with Reinforcement Learning - ICCAD Special Session Paper","publication_year":2021,"publication_date":"2021-11-01","ids":{"openalex":"https://openalex.org/W4200256413","doi":"https://doi.org/10.1109/iccad51958.2021.9643589"},"language":"en","primary_location":{"id":"doi:10.1109/iccad51958.2021.9643589","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccad51958.2021.9643589","pdf_url":null,"source":{"id":"https://openalex.org/S4363608354","display_name":"2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)","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/A5029928585","display_name":"Haoxing Ren","orcid":"https://orcid.org/0000-0003-1028-3860"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Haoxing Ren","raw_affiliation_strings":["NVIDIA Corporation"],"affiliations":[{"raw_affiliation_string":"NVIDIA Corporation","institution_ids":["https://openalex.org/I4210127875"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076191186","display_name":"Saad Godil","orcid":"https://orcid.org/0000-0002-6469-2064"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saad Godil","raw_affiliation_strings":["NVIDIA Corporation"],"affiliations":[{"raw_affiliation_string":"NVIDIA Corporation","institution_ids":["https://openalex.org/I4210127875"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010156116","display_name":"Brucek Khailany","orcid":"https://orcid.org/0000-0002-7584-3489"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Brucek Khailany","raw_affiliation_strings":["NVIDIA Corporation"],"affiliations":[{"raw_affiliation_string":"NVIDIA Corporation","institution_ids":["https://openalex.org/I4210127875"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104192580","display_name":"Robert Kirby","orcid":"https://orcid.org/0000-0002-4588-3922"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Robert Kirby","raw_affiliation_strings":["NVIDIA Corporation"],"affiliations":[{"raw_affiliation_string":"NVIDIA Corporation","institution_ids":["https://openalex.org/I4210127875"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090203011","display_name":"Haiguang Liao","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haiguang Liao","raw_affiliation_strings":["Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004753161","display_name":"R. Nath","orcid":"https://orcid.org/0000-0003-0207-9261"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siddhartha Nath","raw_affiliation_strings":["NVIDIA Corporation"],"affiliations":[{"raw_affiliation_string":"NVIDIA Corporation","institution_ids":["https://openalex.org/I4210127875"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058844317","display_name":"Jonathan Raiman","orcid":"https://orcid.org/0009-0000-5229-7318"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jonathan Raiman","raw_affiliation_strings":["NVIDIA Corporation"],"affiliations":[{"raw_affiliation_string":"NVIDIA Corporation","institution_ids":["https://openalex.org/I4210127875"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032799025","display_name":"Rajarshi Roy","orcid":"https://orcid.org/0000-0003-4548-2114"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rajarshi Roy","raw_affiliation_strings":["NVIDIA Corporation"],"affiliations":[{"raw_affiliation_string":"NVIDIA Corporation","institution_ids":["https://openalex.org/I4210127875"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5029928585"],"corresponding_institution_ids":["https://openalex.org/I4210127875"],"apc_list":null,"apc_paid":null,"fwci":2.1637,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.87993441,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"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/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.9998999834060669,"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/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.9998999834060669,"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/T10551","display_name":"Scheduling and Optimization Algorithms","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T11032","display_name":"VLSI and Analog Circuit Testing","score":0.9965000152587891,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/very-large-scale-integration","display_name":"Very-large-scale integration","score":0.8797534108161926},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7894126176834106},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7723809480667114},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.6258334517478943},{"id":"https://openalex.org/keywords/session","display_name":"Session (web analytics)","score":0.5199528932571411},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.473527193069458},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.44719576835632324},{"id":"https://openalex.org/keywords/placement","display_name":"Placement","score":0.41589492559432983},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3905673325061798},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.2900034189224243},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.2642463743686676},{"id":"https://openalex.org/keywords/physical-design","display_name":"Physical design","score":0.2563712000846863},{"id":"https://openalex.org/keywords/circuit-design","display_name":"Circuit design","score":0.20730826258659363},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1866607964038849},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07963353395462036}],"concepts":[{"id":"https://openalex.org/C14580979","wikidata":"https://www.wikidata.org/wiki/Q876049","display_name":"Very-large-scale integration","level":2,"score":0.8797534108161926},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7894126176834106},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7723809480667114},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.6258334517478943},{"id":"https://openalex.org/C2779182362","wikidata":"https://www.wikidata.org/wiki/Q17126187","display_name":"Session (web analytics)","level":2,"score":0.5199528932571411},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.473527193069458},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.44719576835632324},{"id":"https://openalex.org/C117690923","wikidata":"https://www.wikidata.org/wiki/Q1484784","display_name":"Placement","level":4,"score":0.41589492559432983},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3905673325061798},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.2900034189224243},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.2642463743686676},{"id":"https://openalex.org/C188817802","wikidata":"https://www.wikidata.org/wiki/Q13426855","display_name":"Physical design","level":3,"score":0.2563712000846863},{"id":"https://openalex.org/C190560348","wikidata":"https://www.wikidata.org/wiki/Q3245116","display_name":"Circuit design","level":2,"score":0.20730826258659363},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1866607964038849},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07963353395462036},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccad51958.2021.9643589","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccad51958.2021.9643589","pdf_url":null,"source":{"id":"https://openalex.org/S4363608354","display_name":"2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1944814515","https://openalex.org/W1994685255","https://openalex.org/W2051228319","https://openalex.org/W2122701159","https://openalex.org/W2155968351","https://openalex.org/W2736601468","https://openalex.org/W2746553466","https://openalex.org/W2953334758","https://openalex.org/W2963277051","https://openalex.org/W2979093209","https://openalex.org/W3038962357","https://openalex.org/W3094043398","https://openalex.org/W3095180254","https://openalex.org/W3106804518","https://openalex.org/W3110928049","https://openalex.org/W3140720939","https://openalex.org/W3171752851","https://openalex.org/W3211857759","https://openalex.org/W3211934115","https://openalex.org/W3212488318","https://openalex.org/W6678494045","https://openalex.org/W6683001934","https://openalex.org/W6718092244","https://openalex.org/W6741002519"],"related_works":["https://openalex.org/W4400868993","https://openalex.org/W3096874164","https://openalex.org/W1985560493","https://openalex.org/W2386410636","https://openalex.org/W2357975469","https://openalex.org/W2145363145","https://openalex.org/W1626977535","https://openalex.org/W2341346307","https://openalex.org/W3168977894","https://openalex.org/W4249014856"],"abstract_inverted_index":{"Reinforcement":[0],"learning":[1],"(RL)":[2],"has":[3],"gained":[4],"attention":[5],"recently":[6],"as":[7,21,123],"an":[8],"optimization":[9,28],"algorithm":[10],"for":[11,148],"chip":[12,18],"design.":[13],"This":[14],"method":[15],"treats":[16],"many":[17,79],"design":[19,27,39],"problems":[20,24,82,121],"Markov":[22],"decision":[23],"(MDPs),":[25],"where":[26],"objectives":[29],"are":[30,41,83],"converted":[31,42],"into":[32,43],"rewards":[33],"given":[34],"by":[35],"the":[36,47,134],"environment":[37],"and":[38,59,86,129,143],"variables":[40],"actions":[44],"provided":[45],"to":[46,56,70,92,102,118,139],"environment.":[48],"Some":[49],"recent":[50,113],"examples":[51],"include":[52],"applications":[53],"of":[54,74,136],"RL":[55,66,117,138],"macro":[57],"placement":[58],"standard":[60],"cell":[61,124],"layout":[62],"routing.":[63],"We":[64,132],"believe":[65],"can":[67],"be":[68,90,93],"applied":[69],"nearly":[71],"all":[72],"aspects":[73],"VLSI":[75,80,119,140],"implementation":[76,81,120,141],"flows,":[77],"since":[78],"often":[84],"NP-complete":[85],"state-of-art":[87],"algorithms":[88],"cannot":[89],"guaranteed":[91],"optimal.":[94],"With":[95],"enough":[96],"training":[97],"data,":[98],"it":[99],"is":[100],"possible":[101],"achieve":[103],"better":[104],"results":[105],"with":[106],"RL.":[107],"In":[108],"this":[109],"paper":[110],"we":[111],"review":[112],"advances":[114],"in":[115],"applying":[116,137],"such":[122],"layout,":[125],"synthesis,":[126],"placement,":[127],"routing":[128],"parameter":[130],"tuning.":[131],"discuss":[133],"challenges":[135],"flows":[142],"propose":[144],"future":[145],"research":[146],"directions":[147],"overcoming":[149],"these":[150],"challenges.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}