{"id":"https://openalex.org/W4200141518","doi":"https://doi.org/10.1109/iccad51958.2021.9643435","title":"Doomed Run Prediction in Physical Design by Exploiting Sequential Flow and Graph Learning","display_name":"Doomed Run Prediction in Physical Design by Exploiting Sequential Flow and Graph Learning","publication_year":2021,"publication_date":"2021-11-01","ids":{"openalex":"https://openalex.org/W4200141518","doi":"https://doi.org/10.1109/iccad51958.2021.9643435"},"language":"en","primary_location":{"id":"doi:10.1109/iccad51958.2021.9643435","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccad51958.2021.9643435","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/A5021997733","display_name":"Yi\u2010Chen Lu","orcid":"https://orcid.org/0000-0003-1481-9167"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yi-Chen Lu","raw_affiliation_strings":["School of ECE, Georgia Institute of Technology, Atlanta, GA"],"affiliations":[{"raw_affiliation_string":"School of ECE, Georgia Institute of Technology, Atlanta, GA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Siddhartha Nath","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":false,"raw_author_name":"Siddhartha Nath","raw_affiliation_strings":["Synopsys Inc., Mountain View, CA"],"affiliations":[{"raw_affiliation_string":"Synopsys Inc., Mountain View, CA","institution_ids":["https://openalex.org/I4210088951"]}]},{"author_position":"middle","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":false,"raw_author_name":"Vishal Khandelwal","raw_affiliation_strings":["Synopsys Inc., Hillsboro, OR"],"affiliations":[{"raw_affiliation_string":"Synopsys Inc., Hillsboro, OR","institution_ids":["https://openalex.org/I4210088951"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052950521","display_name":"Sung Kyu Lim","orcid":"https://orcid.org/0000-0002-2267-5282"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sung Kyu Lim","raw_affiliation_strings":["School of ECE, Georgia Institute of Technology, Atlanta, GA"],"affiliations":[{"raw_affiliation_string":"School of ECE, Georgia Institute of Technology, Atlanta, GA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5021997733"],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":null,"apc_paid":null,"fwci":7.3684,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.98800218,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.9995999932289124,"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":0.9995999932289124,"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/T11032","display_name":"VLSI and Analog Circuit Testing","score":0.9988999962806702,"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/T10363","display_name":"Low-power high-performance VLSI design","score":0.9975000023841858,"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":0.7660872936248779},{"id":"https://openalex.org/keywords/netlist","display_name":"Netlist","score":0.6926943063735962},{"id":"https://openalex.org/keywords/design-flow","display_name":"Design flow","score":0.4948400855064392},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.4419448971748352},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.43969541788101196},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43288546800613403},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3466186225414276},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3341867923736572},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.30791786313056946},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.1432400941848755}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7660872936248779},{"id":"https://openalex.org/C177650935","wikidata":"https://www.wikidata.org/wiki/Q1760303","display_name":"Netlist","level":2,"score":0.6926943063735962},{"id":"https://openalex.org/C37135326","wikidata":"https://www.wikidata.org/wiki/Q931942","display_name":"Design flow","level":2,"score":0.4948400855064392},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.4419448971748352},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.43969541788101196},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43288546800613403},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3466186225414276},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3341867923736572},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.30791786313056946},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.1432400941848755},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccad51958.2021.9643435","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccad51958.2021.9643435","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":[{"score":0.49000000953674316,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1974368829","https://openalex.org/W2064675550","https://openalex.org/W2187089797","https://openalex.org/W2293147384","https://openalex.org/W2792643794","https://openalex.org/W2899885603","https://openalex.org/W2946116851","https://openalex.org/W2963285578","https://openalex.org/W2998169401","https://openalex.org/W3012124806","https://openalex.org/W3048124756","https://openalex.org/W3092072718","https://openalex.org/W3111269108","https://openalex.org/W3112618461","https://openalex.org/W3113078536","https://openalex.org/W3116677748","https://openalex.org/W3136792145","https://openalex.org/W3137672172","https://openalex.org/W3161923038","https://openalex.org/W3169517138","https://openalex.org/W4210257598","https://openalex.org/W4238447347","https://openalex.org/W4252138582","https://openalex.org/W4288419263","https://openalex.org/W4294558607","https://openalex.org/W6631190155","https://openalex.org/W6685562342","https://openalex.org/W6738964360","https://openalex.org/W6749029207","https://openalex.org/W6752312381","https://openalex.org/W6760045743","https://openalex.org/W6807384801"],"related_works":["https://openalex.org/W2123076670","https://openalex.org/W2126475478","https://openalex.org/W2543290882","https://openalex.org/W2157878629","https://openalex.org/W2326620043","https://openalex.org/W2024133544","https://openalex.org/W2526357853","https://openalex.org/W4245336546","https://openalex.org/W2038511870","https://openalex.org/W2075511834"],"abstract_inverted_index":{"Modern":[0],"designs":[1,207,243],"are":[2,46,245],"increasingly":[3],"reliant":[4],"on":[5,112,198,204,239],"physical":[6],"design":[7,234],"(PD)":[8],"tools":[9],"to":[10,99,121,177,193],"derive":[11],"full":[12],"technology":[13],"scaling":[14],"benefits":[15],"of":[16,38,76,90,115],"Moore's":[17],"Law.":[18],"Designers":[19],"often":[20],"perform":[21,194],"power,":[22],"performance,":[23],"and":[24,52,186],"area":[25],"(PPA)":[26],"exploration":[27,37],"through":[28],"parallel":[29],"PD":[30,78,104,124,143,163,184],"runs":[31,125],"with":[32,48,208],"different":[33],"tool":[34],"configurations.":[35],"Efficient":[36],"PPA":[39,71],"is":[40,120],"mission-critical":[41],"for":[42],"chip":[43],"designers":[44],"who":[45],"working":[47],"stringent":[49],"time-to-market":[50],"constraints":[51],"finite":[53],"compute":[54],"resources.":[55],"Therefore,":[56],"a":[57,63,82,166],"framework":[58,156,171,216],"that":[59,126,157,214,244],"can":[60,80,96],"accurately":[61],"predict":[62],"\u201cdoomed":[64],"run\u201d":[65],"(i.e.,":[66],"will":[67],"not":[68],"meet":[69],"the":[70,77,113,123,134,199,240],"targets)":[72],"at":[73],"early":[74,88,142,233],"phases":[75],"flow":[79],"provide":[81],"significant":[83],"productivity":[84],"boost":[85],"by":[86,132,161],"enabling":[87],"termination":[89],"such":[91],"runs.":[92,105],"Multiple":[93],"QoR":[94],"metrics":[95],"be":[97],"leveraged":[98],"classify":[100],"successful":[101],"or":[102],"doomed":[103],"In":[106],"this":[107],"paper,":[108],"we":[109,149],"specifically":[110],"focus":[111],"aspect":[114],"timing,":[116],"where":[117],"our":[118,147,170,215],"goal":[119],"identify":[122],"cannot":[127],"achieve":[128,146],"end-of-flow":[129],"timing":[130],"results":[131,203],"predicting":[133],"post-route":[135,218],"total":[136],"negative":[137],"slack":[138],"(TNS)":[139],"values":[140,220],"in":[141,221,232],"phases.":[144],"To":[145],"goal,":[148],"develop":[150],"an":[151],"end-to-end":[152],"machine":[153],"learning":[154],"(ML)":[155],"performs":[158],"TNS":[159,219],"prediction":[160],"modeling":[162,196],"implementation":[164],"as":[165],"sequential":[167,195],"flow.":[168],"Particularly,":[169],"leverages":[172],"graph":[173],"neural":[174],"networks":[175,192],"(GNNs)":[176],"encode":[178],"netlist":[179],"graphs":[180],"extracted":[181],"from":[182],"various":[183],"phases,":[185],"utilize":[187],"long":[188],"short-term":[189],"memory":[190],"(LSTM)":[191],"based":[197],"GNN-encoded":[200],"features.":[201],"Experimental":[202],"seven":[205],"industrial":[206],"5:2":[209],"train/test":[210],"split":[211],"ratio":[212],"demonstrate":[213],"predicts":[217],"high":[222],"fidelity":[223],"within":[224],"5.2%":[225],"normalized":[226],"root":[227],"mean":[228],"squared":[229],"error":[230],"(NRMSE)":[231],"stages":[235],"(e.g.,":[236],"placement,":[237],"CTS)":[238],"two":[241],"validation":[242],"unseen":[246],"during":[247],"training.":[248]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":7}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
