{"id":"https://openalex.org/W4297822218","doi":"https://doi.org/10.1145/3551901.3556490","title":"SpeedER: A Supervised Encoder-Decoder Driven Engine for Effective Resistance Estimation of Power Delivery Networks","display_name":"SpeedER: A Supervised Encoder-Decoder Driven Engine for Effective Resistance Estimation of Power Delivery Networks","publication_year":2022,"publication_date":"2022-09-06","ids":{"openalex":"https://openalex.org/W4297822218","doi":"https://doi.org/10.1145/3551901.3556490"},"language":"en","primary_location":{"id":"doi:10.1145/3551901.3556490","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3551901.3556490","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD","raw_type":"proceedings-article"},"type":"conference-paper","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/A5078626734","display_name":"Bing-Yue Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I154864474","display_name":"National Taiwan University of Science and Technology","ror":"https://ror.org/00q09pe49","country_code":"TW","type":"education","lineage":["https://openalex.org/I154864474"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Bing-Yue Wu","raw_affiliation_strings":["National Taiwan University of Science and Technology, Taipei, Taiwan Roc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Taiwan University of Science and Technology, Taipei, Taiwan Roc","institution_ids":["https://openalex.org/I154864474"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065439030","display_name":"Shao\u2010Yun Fang","orcid":"https://orcid.org/0000-0001-6675-2676"},"institutions":[{"id":"https://openalex.org/I154864474","display_name":"National Taiwan University of Science and Technology","ror":"https://ror.org/00q09pe49","country_code":"TW","type":"education","lineage":["https://openalex.org/I154864474"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Shao-Yun Fang","raw_affiliation_strings":["National Taiwan University of Science and Technology, Taipei, Taiwan Roc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Taiwan University of Science and Technology, Taipei, Taiwan Roc","institution_ids":["https://openalex.org/I154864474"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047251588","display_name":"Hsiang-Wen Chang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hsiang-Wen Chang","raw_affiliation_strings":["Synopsys Taiwan Co., Ltd., Taipei, Taiwan Roc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Synopsys Taiwan Co., Ltd., Taipei, Taiwan Roc","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039979461","display_name":"Peter Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peter Wei","raw_affiliation_strings":["Synopsys Taiwan Co., Ltd., Taipei, Taiwan Roc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Synopsys Taiwan Co., Ltd., Taipei, Taiwan Roc","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3056,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.45928213,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"55","last_page":"61"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10663","display_name":"Advanced Battery Technologies Research","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T10663","display_name":"Advanced Battery Technologies Research","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T11444","display_name":"Electromagnetic Compatibility and Noise Suppression","score":0.9868000149726868,"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/T11522","display_name":"VLSI and FPGA Design Techniques","score":0.9805999994277954,"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":0.7243370413780212},{"id":"https://openalex.org/keywords/solver","display_name":"Solver","score":0.6658133864402771},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.6222891807556152},{"id":"https://openalex.org/keywords/chip","display_name":"Chip","score":0.5252432823181152},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5160048007965088},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5084173083305359},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5049319863319397},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.4671682119369507},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.45417237281799316},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.34297996759414673},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33316075801849365},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1385793387889862}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7243370413780212},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.6658133864402771},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.6222891807556152},{"id":"https://openalex.org/C165005293","wikidata":"https://www.wikidata.org/wiki/Q1074500","display_name":"Chip","level":2,"score":0.5252432823181152},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5160048007965088},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5084173083305359},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5049319863319397},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4671682119369507},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.45417237281799316},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.34297996759414673},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33316075801849365},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1385793387889862},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3551901.3556490","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3551901.3556490","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8199999928474426,"id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G830142704","display_name":null,"funder_award_id":"110-2223-E-011-002-MY3","funder_id":"https://openalex.org/F4320331164","funder_display_name":"National Science and Technology Council"}],"funders":[{"id":"https://openalex.org/F4320331164","display_name":"National Science and Technology Council","ror":"https://ror.org/00wnb9798"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2181518697","https://openalex.org/W2296514843","https://openalex.org/W2607159714","https://openalex.org/W2900015380","https://openalex.org/W2953023533","https://openalex.org/W3036319002","https://openalex.org/W3098698120"],"related_works":["https://openalex.org/W2186864281","https://openalex.org/W4255427455","https://openalex.org/W4390516098","https://openalex.org/W1966025497","https://openalex.org/W68941528","https://openalex.org/W2181948922","https://openalex.org/W4206451355","https://openalex.org/W8322802","https://openalex.org/W2384362569","https://openalex.org/W314331466"],"abstract_inverted_index":{"Voltage":[0],"(IR)":[1],"analysis":[2,41,52],"tools":[3,42],"need":[4],"to":[5,58,84,155,198],"be":[6,85,156,213],"launched":[7],"multiple":[8],"times":[9,216],"during":[10],"the":[11,18,30,61,73,88,113,122,126,130,142,145,151,164,173,201],"Engineering":[12],"Change":[13],"Order":[14],"(ECO)":[15],"phase":[16],"in":[17,112,158],"modern":[19],"design":[20],"cycle":[21],"for":[22,162],"Power":[23],"Delivery":[24],"Network":[25,192],"(PDN)":[26],"refinement,":[27],"while":[28],"analyzing":[29],"IR":[31,40,51],"characteristics":[32],"of":[33,77,87,121,128,144,166,203,228],"advanced":[34],"chip":[35,146],"designs":[36],"by":[37,134],"using":[38,135,222],"traditional":[39,136],"suffers":[43],"from":[44,60],"massive":[45],"run-time.":[46],"Multiple":[47],"Machine":[48],"Learning":[49],"(ML)-driven":[50],"approaches":[53],"have":[54],"been":[55,82],"frequently":[56],"proposed":[57],"benefit":[59],"fast":[62],"inference":[63],"time":[64],"and":[65,98,187],"flexible":[66],"prediction":[67,100],"ability.":[68],"Among":[69],"these":[70],"ML-driven":[71,179],"approaches,":[72],"Effective":[74],"Resistance":[75],"(effR)":[76],"a":[78,159,184,188,219,223],"given":[79],"PDN":[80,123,153],"has":[81],"shown":[83],"one":[86],"most":[89],"critical":[90],"features":[91,197],"that":[92,182,210],"can":[93,212],"greatly":[94],"enhance":[95],"model":[96,186],"performance":[97],"thus":[99],"accuracy;":[101],"however,":[102],"calculating":[103],"effR":[104,132,165],"alone":[105],"is":[106,149],"still":[107],"computationally":[108],"expensive.":[109],"In":[110],"addition,":[111],"ECO":[114],"phase,":[115],"even":[116],"if":[117],"only":[118,229],"local":[119],"adjustments":[120],"are":[124],"required,":[125],"run-time":[127],"obtaining":[129],"regional":[131,205],"changes":[133],"Laplacian":[137,160,224],"Systems":[138],"grows":[139],"exponentially":[140],"as":[141],"size":[143],"grows.":[147],"It":[148],"because":[150],"whole":[152],"needs":[154],"considered":[157],"solver":[161],"computing":[163],"any":[167],"single":[168],"network":[169],"node.":[170],"To":[171],"address":[172],"problem,":[174],"this":[175],"paper":[176],"proposes":[177],"an":[178],"engine,":[180],"SpeedER,":[181],"combines":[183],"U-Net":[185],"Fully":[189],"Connected":[190],"Neural":[191],"(FCNN)":[193],"with":[194,226],"five":[195],"selected":[196],"speed":[199],"up":[200],"process":[202],"estimating":[204],"effRs.":[206],"Experimental":[207],"results":[208],"show":[209],"SpeedER":[211],"approximately":[214],"four":[215],"faster":[217],"than":[218],"commercial":[220],"tool":[221],"System":[225],"errors":[227],"around":[230],"1%.":[231]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
