{"id":"https://openalex.org/W4225162755","doi":"https://doi.org/10.1145/3489517.3530600","title":"Worst-case dynamic power distribution network noise prediction using convolutional neural network","display_name":"Worst-case dynamic power distribution network noise prediction using convolutional neural network","publication_year":2022,"publication_date":"2022-07-10","ids":{"openalex":"https://openalex.org/W4225162755","doi":"https://doi.org/10.1145/3489517.3530600"},"language":"en","primary_location":{"id":"doi:10.1145/3489517.3530600","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3489517.3530600","pdf_url":null,"source":{"id":"https://openalex.org/S4363608816","display_name":"Proceedings of the 59th ACM/IEEE Design Automation Conference","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":"Proceedings of the 59th ACM/IEEE Design Automation Conference","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/A5103163128","display_name":"Xiao Dong","orcid":"https://orcid.org/0000-0002-5993-7776"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Dong","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100411560","display_name":"Yufei Chen","orcid":"https://orcid.org/0000-0003-4947-2917"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yufei Chen","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071383109","display_name":"Xunzhao Yin","orcid":"https://orcid.org/0000-0003-4656-9545"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xunzhao Yin","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100510432","display_name":"Zhuo Cheng","orcid":"https://orcid.org/0000-0001-6924-7579"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Zhuo","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1225","last_page":"1230"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11444","display_name":"Electromagnetic Compatibility and Noise Suppression","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/T11444","display_name":"Electromagnetic Compatibility and Noise Suppression","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/T12495","display_name":"Electrostatic Discharge in Electronics","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/T10363","display_name":"Low-power high-performance VLSI design","score":0.9979000091552734,"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":0.7402132153511047},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6096555590629578},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5881720185279846},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5574489235877991},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.498476505279541},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.4716586172580719},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.45858973264694214},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45470505952835083},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3682969808578491},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.34801387786865234},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.09385368227958679}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7402132153511047},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6096555590629578},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5881720185279846},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5574489235877991},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.498476505279541},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.4716586172580719},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.45858973264694214},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45470505952835083},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3682969808578491},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.34801387786865234},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.09385368227958679},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"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/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3489517.3530600","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3489517.3530600","pdf_url":null,"source":{"id":"https://openalex.org/S4363608816","display_name":"Proceedings of the 59th ACM/IEEE Design Automation Conference","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":"Proceedings of the 59th ACM/IEEE Design Automation Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2030651811","https://openalex.org/W2061930789","https://openalex.org/W2085677136","https://openalex.org/W2099647435","https://openalex.org/W2110295511","https://openalex.org/W2160055399","https://openalex.org/W2168650220","https://openalex.org/W2807453315","https://openalex.org/W2900015380","https://openalex.org/W2945804086","https://openalex.org/W2996794321","https://openalex.org/W3036319002","https://openalex.org/W3087581252","https://openalex.org/W3109998468","https://openalex.org/W3213035978","https://openalex.org/W6631190155"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W98480971","https://openalex.org/W2150291671","https://openalex.org/W2027972911","https://openalex.org/W2146343568","https://openalex.org/W2013643406","https://openalex.org/W2157978810","https://openalex.org/W2597809628","https://openalex.org/W1966837078"],"abstract_inverted_index":{"Worst-case":[0],"dynamic":[1,89,128],"PDN":[2,10,24,41,90,104,129],"noise":[3,47,63,91],"analysis":[4],"is":[5],"an":[6,81],"essential":[7],"step":[8],"in":[9,102],"sign-off":[11],"to":[12,29,38,43,124],"ensure":[13],"the":[14,22,45,87,97,103,126,135,140,144],"performance":[15],"and":[16,26,36,83,99,105,109,143,155],"reliability":[17],"of":[18,66],"chips.":[19],"However,":[20],"with":[21,149],"growing":[23],"size":[25],"increasing":[27],"scenarios":[28],"be":[30],"validated,":[31],"it":[32],"becomes":[33],"very":[34],"time-":[35],"resource-consuming":[37],"conduct":[39],"full-stack":[40],"simulation":[42],"check":[44],"worst-case":[46,88,127],"for":[48,61,86],"different":[49],"test":[50],"vectors.":[51],"Recently,":[52],"various":[53],"works":[54],"have":[55],"proposed":[56,80,136],"machine":[57,146],"learning":[58,147],"based":[59],"methods":[60],"supply":[62],"prediction,":[64],"many":[65],"which":[67],"still":[68],"suffer":[69],"from":[70],"large":[71],"training":[72],"overhead,":[73],"inefficiency,":[74],"or":[75],"non-scalability.":[76],"Thus,":[77],"this":[78],"paper":[79],"efficient":[82,112],"scalable":[84],"framework":[85,94,137],"prediction.":[92],"The":[93],"first":[95],"reduces":[96],"spatial":[98],"temporal":[100],"redundancy":[101],"input":[106],"current":[107],"vector,":[108],"then":[110],"employs":[111],"feature":[113],"extraction":[114],"as":[115,117],"well":[116],"a":[118],"novel":[119],"convolutional":[120],"neural":[121],"network":[122],"architecture":[123],"predict":[125],"noise.":[130],"Experimental":[131],"results":[132],"show":[133],"that":[134],"consistently":[138],"outperforms":[139],"commercial":[141],"tool":[142],"state-of-the-art":[145],"method":[148],"only":[150],"0.63--1.02%":[151],"mean":[152],"relative":[153],"error":[154],"25--69\u00d7":[156],"speedup.":[157]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}