{"id":"https://openalex.org/W4414198739","doi":"https://doi.org/10.1109/dac63849.2025.11132813","title":"To Tackle Cost-Skew Tradeoff: An Adaptive Learning Approach for Hub Node Selection","display_name":"To Tackle Cost-Skew Tradeoff: An Adaptive Learning Approach for Hub Node Selection","publication_year":2025,"publication_date":"2025-06-22","ids":{"openalex":"https://openalex.org/W4414198739","doi":"https://doi.org/10.1109/dac63849.2025.11132813"},"language":"en","primary_location":{"id":"doi:10.1109/dac63849.2025.11132813","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac63849.2025.11132813","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 62nd ACM/IEEE Design Automation Conference (DAC)","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":null,"display_name":"Guowei Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Guowei Sun","raw_affiliation_strings":["School of Data Science"],"affiliations":[{"raw_affiliation_string":"School of Data Science","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100443812","display_name":"Lin Chen","orcid":"https://orcid.org/0000-0003-3899-535X"},"institutions":[{"id":"https://openalex.org/I4390039265","display_name":"PRG S&Tech (South Korea)","ror":"https://ror.org/02sr2ee22","country_code":null,"type":"company","lineage":["https://openalex.org/I4390039265"]}],"countries":[],"is_corresponding":false,"raw_author_name":"Lin Chen","raw_affiliation_strings":["School of Computer Science and Technology"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology","institution_ids":["https://openalex.org/I4390039265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102843024","display_name":"Qiming Huang","orcid":"https://orcid.org/0009-0004-3413-1954"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiming Huang","raw_affiliation_strings":["School of the Gifted Young University of Science and Technology of China,Hefei,China,230026"],"affiliations":[{"raw_affiliation_string":"School of the Gifted Young University of Science and Technology of China,Hefei,China,230026","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045416081","display_name":"Hu Ding","orcid":"https://orcid.org/0000-0002-1814-1552"},"institutions":[{"id":"https://openalex.org/I4390039265","display_name":"PRG S&Tech (South Korea)","ror":"https://ror.org/02sr2ee22","country_code":null,"type":"company","lineage":["https://openalex.org/I4390039265"]}],"countries":[],"is_corresponding":false,"raw_author_name":"Hu Ding","raw_affiliation_strings":["School of Computer Science and Technology"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology","institution_ids":["https://openalex.org/I4390039265"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26621368,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11201","display_name":"Metallurgy and Material Forming","score":0.0908999964594841,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/T11201","display_name":"Metallurgy and Material Forming","score":0.0908999964594841,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/key","display_name":"Key (lock)","score":0.6732000112533569},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6410999894142151},{"id":"https://openalex.org/keywords/skew","display_name":"Skew","score":0.567799985408783},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5511000156402588},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5490999817848206},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.46219998598098755},{"id":"https://openalex.org/keywords/adaptive-learning","display_name":"Adaptive learning","score":0.4050000011920929}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7487000226974487},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.6732000112533569},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6410999894142151},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.567799985408783},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5511000156402588},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5490999817848206},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49470001459121704},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46869999170303345},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.46219998598098755},{"id":"https://openalex.org/C125014702","wikidata":"https://www.wikidata.org/wiki/Q4680749","display_name":"Adaptive learning","level":2,"score":0.4050000011920929},{"id":"https://openalex.org/C2781039887","wikidata":"https://www.wikidata.org/wiki/Q1391724","display_name":"Factor (programming language)","level":2,"score":0.367900013923645},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.36320000886917114},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.33660000562667847},{"id":"https://openalex.org/C24856439","wikidata":"https://www.wikidata.org/wiki/Q352483","display_name":"Adaptive routing","level":5,"score":0.3009999990463257},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.26739999651908875},{"id":"https://openalex.org/C52970973","wikidata":"https://www.wikidata.org/wiki/Q2497134","display_name":"Adaptive system","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2565999925136566},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.25619998574256897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dac63849.2025.11132813","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac63849.2025.11132813","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 62nd ACM/IEEE Design Automation Conference (DAC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1507707340","https://openalex.org/W1944814515","https://openalex.org/W2020112047","https://openalex.org/W2021692155","https://openalex.org/W2034999260","https://openalex.org/W2041176678","https://openalex.org/W2060400450","https://openalex.org/W2064086982","https://openalex.org/W2069142841","https://openalex.org/W2125831674","https://openalex.org/W2128237591","https://openalex.org/W2156391618","https://openalex.org/W2790830285","https://openalex.org/W2810257922","https://openalex.org/W3100789280","https://openalex.org/W3211880575","https://openalex.org/W4213075593","https://openalex.org/W4250643412","https://openalex.org/W4377969929","https://openalex.org/W4385245566","https://openalex.org/W4389160770"],"related_works":[],"abstract_inverted_index":{"In":[0,45],"chip":[1],"design,":[2],"skew":[3],"is":[4,19,36,63,73],"a":[5,50],"pivotal":[6],"factor":[7],"that":[8,85],"significantly":[9],"influences":[10],"the":[11,27],"overall":[12],"performance":[13,91],"for":[14,55,76],"routing.":[15],"A":[16],"major":[17],"challenge":[18],"how":[20],"to":[21,40],"achieve":[22,89],"an":[23,37,65],"appropriate":[24],"trade-off":[25],"between":[26],"total":[28],"wire-length":[29],"cost":[30],"and":[31,95],"skew.":[32],"Selecting":[33],"hub":[34,56],"nodes":[35],"effective":[38,66],"method":[39,54,87],"improve":[41],"this":[42,46],"cost-skew":[43],"trade-off.":[44],"paper,":[47],"we":[48],"propose":[49],"novel":[51],"reinforcement":[52],"learning-based":[53],"node":[57],"selection,":[58],"where":[59],"our":[60,71,86],"key":[61],"idea":[62],"leveraging":[64],"adaptive":[67],"learning":[68],"strategy.":[69],"Moreover,":[70],"approach":[72],"particularly":[74],"suitable":[75],"solving":[77],"large-scale":[78,96],"routing":[79],"instances.":[80],"The":[81],"empirical":[82],"results":[83],"suggest":[84],"can":[88],"promising":[90],"on":[92],"both":[93],"small-scale":[94],"clock":[97],"nets,":[98],"implying":[99],"its":[100],"potential":[101],"practical":[102],"significance":[103],"in":[104],"EDA.":[105]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
