{"id":"https://openalex.org/W4404134074","doi":"https://doi.org/10.1145/3649329.3658465","title":"MAUnet: Multiscale Attention U-Net for Effective IR Drop Prediction","display_name":"MAUnet: Multiscale Attention U-Net for Effective IR Drop Prediction","publication_year":2024,"publication_date":"2024-06-23","ids":{"openalex":"https://openalex.org/W4404134074","doi":"https://doi.org/10.1145/3649329.3658465"},"language":"en","primary_location":{"id":"doi:10.1145/3649329.3658465","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3649329.3658465","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 61st ACM/IEEE Design Automation Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://eprints.whiterose.ac.uk/221017/1/DAC2024_MAUnet__Camera_ready___0404___4_.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069313457","display_name":"Mingyue Wang","orcid":"https://orcid.org/0009-0005-6222-1778"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingyue Wang","raw_affiliation_strings":["BeiHang University, Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0005-6222-1778","affiliations":[{"raw_affiliation_string":"BeiHang University, Beijing, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091884273","display_name":"Yuanqing Cheng","orcid":"https://orcid.org/0000-0003-2477-314X"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanqing Cheng","raw_affiliation_strings":["BeiHang University, Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2477-314X","affiliations":[{"raw_affiliation_string":"BeiHang University, Beijing, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103385972","display_name":"Yuan-Hsu Lin","orcid":"https://orcid.org/0009-0005-2158-3027"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yage Lin","raw_affiliation_strings":["BeiHang University, Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0005-2158-3027","affiliations":[{"raw_affiliation_string":"BeiHang University, Beijing, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Kelin Peng","orcid":"https://orcid.org/0009-0004-1118-2572"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kelin Peng","raw_affiliation_strings":["BeiHang University, Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0004-1118-2572","affiliations":[{"raw_affiliation_string":"BeiHang University, Beijing, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091310658","display_name":"Shunchuan Yang","orcid":"https://orcid.org/0000-0003-2094-6090"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shunchuan Yang","raw_affiliation_strings":["BeiHang University, Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2094-6090","affiliations":[{"raw_affiliation_string":"BeiHang University, Beijing, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057257864","display_name":"Zhou Jin","orcid":"https://orcid.org/0000-0002-0632-9494"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhou Jin","raw_affiliation_strings":["Super Scientific Software Laboratory, China University of Petroleum-Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0632-9494","affiliations":[{"raw_affiliation_string":"Super Scientific Software Laboratory, China University of Petroleum-Beijing, Beijing, China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020806445","display_name":"Wei Xing","orcid":"https://orcid.org/0000-0002-3177-8478"},"institutions":[{"id":"https://openalex.org/I91136226","display_name":"University of Sheffield","ror":"https://ror.org/05krs5044","country_code":"GB","type":"education","lineage":["https://openalex.org/I91136226"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Wei Xing","raw_affiliation_strings":["University of Sheffield, Sheffield, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-3177-8478","affiliations":[{"raw_affiliation_string":"University of Sheffield, Sheffield, United Kingdom","institution_ids":["https://openalex.org/I91136226"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4516,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.8349053,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9860000014305115,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11019","display_name":"Image Enhancement Techniques","score":0.9860000014305115,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12676","display_name":"Machine Learning and ELM","score":0.9635999798774719,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9592000246047974,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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":0.5002899169921875},{"id":"https://openalex.org/keywords/drop","display_name":"Drop (telecommunication)","score":0.45391303300857544},{"id":"https://openalex.org/keywords/net","display_name":"Net (polyhedron)","score":0.4155412018299103},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33668261766433716},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14369681477546692},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.05943801999092102}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5002899169921875},{"id":"https://openalex.org/C2781345722","wikidata":"https://www.wikidata.org/wiki/Q5308388","display_name":"Drop (telecommunication)","level":2,"score":0.45391303300857544},{"id":"https://openalex.org/C14166107","wikidata":"https://www.wikidata.org/wiki/Q253829","display_name":"Net (polyhedron)","level":2,"score":0.4155412018299103},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33668261766433716},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14369681477546692},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.05943801999092102},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3649329.3658465","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3649329.3658465","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 61st ACM/IEEE Design Automation Conference","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.whiterose.ac.uk:221017","is_oa":true,"landing_page_url":"https://orcid.org/0009-0005-6222-1778>,","pdf_url":"https://eprints.whiterose.ac.uk/221017/1/DAC2024_MAUnet__Camera_ready___0404___4_.pdf","source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Proceedings Paper"}],"best_oa_location":{"id":"pmh:oai:eprints.whiterose.ac.uk:221017","is_oa":true,"landing_page_url":"https://orcid.org/0009-0005-6222-1778>,","pdf_url":"https://eprints.whiterose.ac.uk/221017/1/DAC2024_MAUnet__Camera_ready___0404___4_.pdf","source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Proceedings Paper"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4404134074.pdf"},"referenced_works_count":12,"referenced_works":["https://openalex.org/W638556532","https://openalex.org/W1598246754","https://openalex.org/W1901129140","https://openalex.org/W1971734459","https://openalex.org/W2133342289","https://openalex.org/W2133548584","https://openalex.org/W3013164405","https://openalex.org/W3036319002","https://openalex.org/W3100761977","https://openalex.org/W3127316871","https://openalex.org/W3127939301","https://openalex.org/W4200338900"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"The":[0,68,87,160],"efficient":[1],"analysis":[2,35],"of":[3,24,101,147,179,220],"power":[4,21,115],"grids":[5,116],"is":[6,190],"a":[7,108,123],"crucial":[8],"yet":[9],"computationally":[10],"challenging":[11],"task":[12],"in":[13,184,226],"integrated":[14],"circuit":[15,201],"(IC)":[16],"design,":[17],"given":[18],"the":[19,31,80,91,153,171,215],"shrinking":[20],"supply":[22],"voltage":[23],"ultra":[25],"deep-submicron":[26],"VLSI":[27],"design.":[28],"Different":[29],"from":[30,76],"conventional":[32],"modified":[33],"nodal":[34],"technique,":[36],"this":[37],"paper":[38],"introduces":[39],"MAUnet,":[40],"an":[41,144],"innovative":[42],"machine-learning":[43],"model":[44,194],"that":[45,112],"redefines":[46],"state-of-the-art":[47,173],"full-chip":[48],"static":[49],"IR":[50,155],"drop":[51,156],"prediction.":[52],"MAUnet":[53,105],"ingeniously":[54],"integrates":[55],"multi-scale":[56,69],"convolutional":[57,70],"blocks,":[58],"attention":[59,81],"mechanisms,":[60],"and":[61,117,182],"U-Net":[62,88],"architecture":[63],"to":[64,99,129,133,152,192,195,205,212],"optimize":[65],"prediction":[66,97],"accuracy.":[67],"blocks":[71],"significantly":[72],"enhance":[73],"feature":[74],"extraction":[75],"image-based":[77,118],"data,":[78],"while":[79],"mechanism":[82],"precisely":[83],"identifies":[84],"hotspot":[85],"regions.":[86],"architecture,":[89],"on":[90,157,199],"other":[92],"hand,":[93],"enables":[94],"scalable":[95],"image-to-image":[96],"applicable":[98],"circuits":[100],"any":[102],"size.":[103],"Uniquely,":[104],"also":[106],"incorporates":[107],"pioneering":[109],"fusion":[110],"method":[111,167,217],"synergies":[113],"both":[114],"data.":[119],"Additionally,":[120],"we":[121],"introduce":[122],"low-rank":[124],"approximation":[125],"transfer":[126],"learning":[127,189],"technique":[128],"extend":[130],"MAUnet's":[131,140],"applicability":[132],"unseen":[134],"test":[135,202],"cases.":[136,203],"Benchmark":[137],"tests":[138],"validate":[139],"superior":[141],"performance,":[142],"achieving":[143],"average":[145,154],"error":[146,225],"less":[148],"than":[149],"6%":[150],"relative":[151],"three":[158,185],"benchmarks.":[159,187],"performance":[161],"enhancements":[162],"offered":[163],"by":[164,176],"our":[165],"proposed":[166,216],"are":[168],"substantial,":[169],"outperforming":[170],"current":[172],"method,":[174],"IREDGe,":[175],"considerable":[177],"margins":[178],"29%,":[180],"65%,":[181],"68%":[183],"canonical":[186],"Transfer":[188],"validated":[191],"enable":[193],"achieve":[196],"effective":[197],"improvement":[198],"real":[200],"Compared":[204],"commercial":[206],"tools,":[207],"which":[208],"often":[209],"require":[210],"hours":[211],"deliver":[213],"results,":[214],"provides":[218],"orders":[219],"magnitude":[221],"speed-up":[222],"with":[223],"negligible":[224],"practice.":[227]},"counts_by_year":[{"year":2025,"cited_by_count":7}],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2025-10-10T00:00:00"}