{"id":"https://openalex.org/W4409150781","doi":"https://doi.org/10.1145/3690624.3709185","title":"TransPlace: Transferable Circuit Global Placement via Graph Neural Network","display_name":"TransPlace: Transferable Circuit Global Placement via Graph Neural Network","publication_year":2025,"publication_date":"2025-04-04","ids":{"openalex":"https://openalex.org/W4409150781","doi":"https://doi.org/10.1145/3690624.3709185"},"language":"en","primary_location":{"id":"doi:10.1145/3690624.3709185","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3690624.3709185","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","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/A5100525788","display_name":"Yunbo Hou","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yunbo Hou","raw_affiliation_strings":["School of Software and Microelectronics, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Software and Microelectronics, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015329124","display_name":"Haoran Ye","orcid":"https://orcid.org/0000-0002-8510-3716"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoran Ye","raw_affiliation_strings":["The State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, China, and School of Intelligence Science and Technology, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"The State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, China, and School of Intelligence Science and Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019544963","display_name":"Shuwen Yang","orcid":"https://orcid.org/0000-0003-2951-2918"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shuwen Yang","raw_affiliation_strings":["DP Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"DP Technology, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058247332","display_name":"Yingxue Zhang","orcid":"https://orcid.org/0000-0001-9871-4682"},"institutions":[{"id":"https://openalex.org/I4210115038","display_name":"Huawei Technologies (Canada)","ror":"https://ror.org/026venb53","country_code":"CA","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210115038"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yingxue Zhang","raw_affiliation_strings":["Huawei Noah's Ark Lab, Markham, Ontario, Canada"],"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, Markham, Ontario, Canada","institution_ids":["https://openalex.org/I4210115038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101819650","display_name":"Siyuan Xu","orcid":"https://orcid.org/0000-0001-6239-6774"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siyuan Xu","raw_affiliation_strings":["Huawei Noah's Ark Lab, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088976879","display_name":"Guojie Song","orcid":"https://orcid.org/0000-0001-8295-2520"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guojie Song","raw_affiliation_strings":["The State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, China, and School of Intelligence Science and Technology, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"The State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, China, and School of Intelligence Science and Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100525788"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06603086,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"484","last_page":"495"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11522","display_name":"VLSI and FPGA Design Techniques","score":1.0,"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":1.0,"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":0.9998000264167786,"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":0.9998000264167786,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7025332450866699},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4976220428943634},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4369225800037384},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.28918778896331787},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24772608280181885}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7025332450866699},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4976220428943634},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4369225800037384},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28918778896331787},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24772608280181885}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3690624.3709185","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3690624.3709185","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1985292881","https://openalex.org/W2031974129","https://openalex.org/W2096895065","https://openalex.org/W2102378996","https://openalex.org/W2120985183","https://openalex.org/W2133560740","https://openalex.org/W2161629461","https://openalex.org/W2167190617","https://openalex.org/W2770762651","https://openalex.org/W2778051509","https://openalex.org/W2884307971","https://openalex.org/W2907492528","https://openalex.org/W2909290526","https://openalex.org/W2916090591","https://openalex.org/W2990138404","https://openalex.org/W2992137891","https://openalex.org/W3005126554","https://openalex.org/W3036696276","https://openalex.org/W3104644561","https://openalex.org/W3113752050","https://openalex.org/W3117967161","https://openalex.org/W3125127664","https://openalex.org/W3131660762","https://openalex.org/W3171752851","https://openalex.org/W3174166145","https://openalex.org/W3200609275","https://openalex.org/W3209924365","https://openalex.org/W3211934115","https://openalex.org/W4200141518","https://openalex.org/W4224141805","https://openalex.org/W4226435553","https://openalex.org/W4280492458","https://openalex.org/W4292092927","https://openalex.org/W4293023524","https://openalex.org/W4310269642","https://openalex.org/W4312076525","https://openalex.org/W4312121007","https://openalex.org/W4318684505","https://openalex.org/W4382765876","https://openalex.org/W6841939121"],"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":{"Global":[0],"placement,":[1],"a":[2,50,86,100,113],"critical":[3],"step":[4],"in":[5,62],"designing":[6],"the":[7],"physical":[8],"layout":[9],"of":[10,32,59,95],"computer":[11],"chips,":[12],"is":[13],"essential":[14],"to":[15,56,70,81,107],"optimize":[16,23],"chip":[17,39],"performance.":[18],"Prior":[19],"global":[20,51],"placement":[21,52,96,109],"methods":[22],"each":[24],"circuit":[25,42],"design":[26],"individually":[27],"from":[28],"scratch.":[29],"Their":[30],"neglect":[31],"transferable":[33],"knowledge":[34],"limits":[35],"solution":[36],"efficiency":[37],"and":[38,77,98,131],"performance":[40],"as":[41],"complexity":[43],"drastically":[44],"increases.":[45],"This":[46],"study":[47],"presents":[48],"TransPlace,":[49],"framework":[53],"that":[54],"learns":[55],"place":[57,117],"millions":[58],"mixed-size":[60],"cells":[61],"continuous":[63],"space.":[64],"TransPlace":[65],"introduces":[66],"i)":[67],"Netlist":[68],"Graph":[69],"efficiently":[71],"model":[72],"netlist":[73],"topology,":[74],"ii)":[75],"Cell-flow":[76],"relative":[78],"position":[79],"encoding":[80],"learn":[82],"SE(2)-invariant":[83],"representation,":[84],"iii)":[85],"tailored":[87],"graph":[88],"neural":[89],"network":[90],"architecture":[91],"for":[92,103],"informed":[93],"parameterization":[94],"knowledge,":[97],"iv)":[99],"two-stage":[101],"strategy":[102],"coarse-to-fine":[104],"placement.":[105],"Compared":[106],"state-of-the-art":[108],"methods,":[110],"TransPlace-trained":[111],"on":[112],"few":[114],"high-quality":[115],"placements-can":[116],"unseen":[118],"circuits":[119],"with":[120],"1.2x":[121],"speedup":[122],"while":[123],"reducing":[124],"congestion":[125],"by":[126,129,133],"30%,":[127],"timing":[128],"9%,":[130],"wirelength":[132],"5%.":[134]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}