{"id":"https://openalex.org/W4389160809","doi":"https://doi.org/10.1109/iccad57390.2023.10323767","title":"Invited Paper: 2023 ICCAD CAD Contest Problem C: Static IR Drop Estimation Using Machine Learning","display_name":"Invited Paper: 2023 ICCAD CAD Contest Problem C: Static IR Drop Estimation Using Machine Learning","publication_year":2023,"publication_date":"2023-10-28","ids":{"openalex":"https://openalex.org/W4389160809","doi":"https://doi.org/10.1109/iccad57390.2023.10323767"},"language":"en","primary_location":{"id":"doi:10.1109/iccad57390.2023.10323767","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccad57390.2023.10323767","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 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/A5093382833","display_name":"Gana Surya Prakash Kadagala","orcid":null},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Gana Surya Prakash Kadagala","raw_affiliation_strings":["Arizona State University"],"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069179438","display_name":"Vidya A. Chhabria","orcid":"https://orcid.org/0000-0002-3273-0724"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vidya A. Chhabria","raw_affiliation_strings":["Arizona State University"],"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5093382833"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":2.465,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.89377009,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11032","display_name":"VLSI and Analog Circuit Testing","score":0.9990000128746033,"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/T11032","display_name":"VLSI and Analog Circuit Testing","score":0.9990000128746033,"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/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.9990000128746033,"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":0.9986000061035156,"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/power-network-design","display_name":"Power network design","score":0.8678033947944641},{"id":"https://openalex.org/keywords/contest","display_name":"CONTEST","score":0.7770879864692688},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6992558240890503},{"id":"https://openalex.org/keywords/drop","display_name":"Drop (telecommunication)","score":0.6103187203407288},{"id":"https://openalex.org/keywords/voltage-drop","display_name":"Voltage drop","score":0.5717628598213196},{"id":"https://openalex.org/keywords/chip","display_name":"Chip","score":0.5495204329490662},{"id":"https://openalex.org/keywords/drop-out","display_name":"Drop out","score":0.47720059752464294},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4489530622959137},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4085468351840973},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.40038755536079407},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3328070044517517},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.30913716554641724},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15991875529289246},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.11841148138046265}],"concepts":[{"id":"https://openalex.org/C164565468","wikidata":"https://www.wikidata.org/wiki/Q7236535","display_name":"Power network design","level":3,"score":0.8678033947944641},{"id":"https://openalex.org/C2777582232","wikidata":"https://www.wikidata.org/wiki/Q5013414","display_name":"CONTEST","level":2,"score":0.7770879864692688},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6992558240890503},{"id":"https://openalex.org/C2781345722","wikidata":"https://www.wikidata.org/wiki/Q5308388","display_name":"Drop (telecommunication)","level":2,"score":0.6103187203407288},{"id":"https://openalex.org/C82178898","wikidata":"https://www.wikidata.org/wiki/Q166839","display_name":"Voltage drop","level":3,"score":0.5717628598213196},{"id":"https://openalex.org/C165005293","wikidata":"https://www.wikidata.org/wiki/Q1074500","display_name":"Chip","level":2,"score":0.5495204329490662},{"id":"https://openalex.org/C2984949393","wikidata":"https://www.wikidata.org/wiki/Q1260241","display_name":"Drop out","level":2,"score":0.47720059752464294},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4489530622959137},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4085468351840973},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.40038755536079407},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3328070044517517},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.30913716554641724},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15991875529289246},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.11841148138046265},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C4249254","wikidata":"https://www.wikidata.org/wiki/Q3044431","display_name":"Demographic economics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccad57390.2023.10323767","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccad57390.2023.10323767","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2115314588","https://openalex.org/W2807453315","https://openalex.org/W2946332877","https://openalex.org/W2996794321","https://openalex.org/W3013164405","https://openalex.org/W3013474731","https://openalex.org/W3127939301","https://openalex.org/W3186706634","https://openalex.org/W3210759853","https://openalex.org/W3210851702","https://openalex.org/W4200338900","https://openalex.org/W4206436503","https://openalex.org/W4238320324"],"related_works":["https://openalex.org/W2108172432","https://openalex.org/W2119232911","https://openalex.org/W2143381869","https://openalex.org/W121910558","https://openalex.org/W4255681223","https://openalex.org/W1987230547","https://openalex.org/W3215613000","https://openalex.org/W653153512","https://openalex.org/W2138550049","https://openalex.org/W4245161178"],"abstract_inverted_index":{"Power":[0],"delivery":[1],"network":[2],"(PDN)":[3],"analysis":[4],"is":[5,36,79,184],"a":[6,31,68,160],"critical":[7],"aspect":[8],"of":[9,22,33,41,48,71,76,144,181],"the":[10,15,19,23,39,42,49,60,120,182,192],"design":[11],"cycle":[12],"to":[13,38,66,96,151,185,190],"ensure":[14],"power":[16],"grid":[17],"meets":[18],"current":[20],"demands":[21],"chip.":[24],"Static":[25],"IR":[26,62,109,153],"drop":[27,45,47,63,110],"simulation,":[28,111],"performed":[29],"as":[30,132,139,159],"part":[32],"PDN":[34],"analysis,":[35],"crucial":[37],"estimation":[40],"worstcase":[43],"voltage":[44],"(IR)":[46],"chip":[50,55],"which":[51,163],"in":[52],"turn":[53],"determines":[54],"frequency":[56],"and":[57,78,137,142,199],"functionality.":[58],"Algorithmically,":[59],"static":[61,108],"simulation":[64],"amounts":[65],"solving":[67],"large":[69,85],"system":[70],"linear":[72],"equations":[73],"with":[74,83,194],"billions":[75],"variables":[77],"computationally":[80],"very":[81],"expensive":[82],"significantly":[84],"runtimes.":[86,201],"This":[87],"contest":[88,126,183],"aims":[89],"at":[90],"leveraging":[91],"machine":[92],"learning":[93],"(ML)":[94],"techniques":[95],"overcome":[97],"this":[98],"challenge.":[99],"While":[100],"previous":[101],"research":[102],"has":[103],"introduced":[104],"ML-based":[105],"solutions":[106],"for":[107,147,173],"their":[112],"performance":[113],"remains":[114],"untested":[115],"on":[116,176],"standardized":[117],"benchmarks,":[118],"obscuring":[119],"true":[121],"state-of-the-art":[122],"ML":[123,149,188],"model.":[124],"The":[125,155,179],"releases":[127],"twenty":[128],"real":[129,170],"circuit":[130,171],"(split":[131],"ten":[133,138],"seen":[134],"during":[135],"training":[136,148,161],"hidden)":[140],"benchmarks":[141,146],"hundreds":[143],"synthetic":[145,156],"models":[150],"predict":[152],"drop.":[154],"data":[157,172],"serves":[158],"dataset,":[162],"can":[164],"then":[165],"be":[166],"fine-tuned":[167],"using":[168],"limited":[169],"accurate":[174],"predictions":[175],"unseen":[177],"testcases.":[178],"goal":[180],"train":[186],"novel":[187],"algorithms":[189],"perform":[191],"prediction":[193],"high":[195],"accuracy,":[196],"F1":[197],"score,":[198],"low":[200]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}