{"id":"https://openalex.org/W3108681315","doi":"https://doi.org/10.1145/3380446.3430691","title":"Machine-Learning Enabled Next-Generation Physical Design - An EDA Perspective","display_name":"Machine-Learning Enabled Next-Generation Physical Design - An EDA Perspective","publication_year":2020,"publication_date":"2020-11-16","ids":{"openalex":"https://openalex.org/W3108681315","doi":"https://doi.org/10.1145/3380446.3430691","mag":"3108681315"},"language":"en","primary_location":{"id":"doi:10.1145/3380446.3430691","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3380446.3430691","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":"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/A5110815003","display_name":"Vishal Khandelwal","orcid":null},"institutions":[{"id":"https://openalex.org/I4210088951","display_name":"Synopsys (United States)","ror":"https://ror.org/013by2m91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210088951"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Vishal Khandelwal","raw_affiliation_strings":["Synopsys Inc., Hillsboro, OR, USA"],"affiliations":[{"raw_affiliation_string":"Synopsys Inc., Hillsboro, OR, USA","institution_ids":["https://openalex.org/I4210088951"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5110815003"],"corresponding_institution_ids":["https://openalex.org/I4210088951"],"apc_list":null,"apc_paid":null,"fwci":0.1027,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.44701097,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"135","last_page":"135"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.9128999710083008,"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":0.9128999710083008,"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":0.728191614151001},{"id":"https://openalex.org/keywords/physical-design","display_name":"Physical design","score":0.6456360816955566},{"id":"https://openalex.org/keywords/design-flow","display_name":"Design flow","score":0.549106776714325},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.5308141708374023},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5215677618980408},{"id":"https://openalex.org/keywords/limit","display_name":"Limit (mathematics)","score":0.5178263783454895},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5161443948745728},{"id":"https://openalex.org/keywords/engineering-design-process","display_name":"Engineering design process","score":0.47328561544418335},{"id":"https://openalex.org/keywords/cyber-physical-system","display_name":"Cyber-physical system","score":0.4562044143676758},{"id":"https://openalex.org/keywords/design-process","display_name":"Design process","score":0.45555007457733154},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.4401463568210602},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4246920347213745},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3832731246948242},{"id":"https://openalex.org/keywords/industrial-engineering","display_name":"Industrial engineering","score":0.37919384241104126},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.332755982875824},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3290821313858032},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.2589815855026245},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.2235378921031952},{"id":"https://openalex.org/keywords/work-in-process","display_name":"Work in process","score":0.20797434449195862},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14660334587097168},{"id":"https://openalex.org/keywords/circuit-design","display_name":"Circuit design","score":0.1305750608444214}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.728191614151001},{"id":"https://openalex.org/C188817802","wikidata":"https://www.wikidata.org/wiki/Q13426855","display_name":"Physical design","level":3,"score":0.6456360816955566},{"id":"https://openalex.org/C37135326","wikidata":"https://www.wikidata.org/wiki/Q931942","display_name":"Design flow","level":2,"score":0.549106776714325},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.5308141708374023},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5215677618980408},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.5178263783454895},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5161443948745728},{"id":"https://openalex.org/C34972735","wikidata":"https://www.wikidata.org/wiki/Q2920267","display_name":"Engineering design process","level":2,"score":0.47328561544418335},{"id":"https://openalex.org/C179768478","wikidata":"https://www.wikidata.org/wiki/Q1120057","display_name":"Cyber-physical system","level":2,"score":0.4562044143676758},{"id":"https://openalex.org/C48262172","wikidata":"https://www.wikidata.org/wiki/Q16908765","display_name":"Design process","level":3,"score":0.45555007457733154},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.4401463568210602},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4246920347213745},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3832731246948242},{"id":"https://openalex.org/C13736549","wikidata":"https://www.wikidata.org/wiki/Q4489420","display_name":"Industrial engineering","level":1,"score":0.37919384241104126},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.332755982875824},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3290821313858032},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.2589815855026245},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2235378921031952},{"id":"https://openalex.org/C174998907","wikidata":"https://www.wikidata.org/wiki/Q357662","display_name":"Work in process","level":2,"score":0.20797434449195862},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14660334587097168},{"id":"https://openalex.org/C190560348","wikidata":"https://www.wikidata.org/wiki/Q3245116","display_name":"Circuit design","level":2,"score":0.1305750608444214},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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},{"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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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:10.1145/3380446.3430691","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3380446.3430691","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":"Industry, innovation and infrastructure","score":0.6600000262260437,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4253195573","https://openalex.org/W2020934033","https://openalex.org/W4245336546","https://openalex.org/W2908947570","https://openalex.org/W2133012978","https://openalex.org/W2126983197","https://openalex.org/W2743305891","https://openalex.org/W2123076670","https://openalex.org/W2070693700","https://openalex.org/W2078506771"],"abstract_inverted_index":{"Physical":[0],"design":[1,41,53,134],"is":[2,62],"an":[3,71],"ensemble":[4],"of":[5,97,124],"NP-complete":[6],"problems":[7],"that":[8],"P&R":[9],"tools":[10],"attempt":[11],"to":[12,43,66,74,81,106,121],"solve":[13],"in":[14,26,88,131],"(pseudo)":[15],"linear":[16],"time.":[17],"Advanced":[18],"process":[19],"nodes":[20],"and":[21,29,78,90,127],"complex":[22],"signoff":[23],"requirements":[24],"bring":[25],"new":[27],"physical":[28,40,133],"timing":[30],"constraints":[31],"into":[32],"the":[33,86,95,104,125],"implementation":[34,83],"flow,":[35],"making":[36],"it":[37],"harder":[38],"for":[39,58,112,129],"algorithms":[42],"deliver":[44],"industry-leading":[45],"power,":[46],"performance,":[47],"area":[48],"(PPA),":[49],"without":[50],"giving":[51],"up":[52],"turn-around-time.":[54],"The":[55],"relentless":[56],"pursuit":[57],"low-power":[59],"high-performance":[60],"designs":[61],"putting":[63],"constant":[64],"pressure":[65],"limit":[67],"any":[68],"over-design,":[69],"creating":[70],"acute":[72],"need":[73],"have":[75],"better":[76],"models/predictions":[77],"advanced":[79],"analytics":[80],"drive":[82],"flows.":[84],"Given":[85],"advancements":[87],"supervised":[89],"reinforcement":[91],"learning,":[92],"combined":[93],"with":[94],"availability":[96],"large-scale":[98],"compute,":[99],"Machine":[100],"Learning":[101],"(ML)":[102],"has":[103],"potential":[105],"become":[107],"a":[108],"disruptive":[109],"paradigm":[110],"change":[111],"EDA":[113],"tools.":[114],"In":[115],"this":[116],"talk,":[117],"I":[118],"would":[119],"like":[120],"share":[122],"some":[123],"challenges":[126],"opportunities":[128],"innovation":[130],"next-generation":[132],"using":[135],"ML.":[136]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}