{"id":"https://openalex.org/W4411713434","doi":"https://doi.org/10.1145/3716368.3735237","title":"Overcoming Training Data Scarcity in Routing Demand Prediction via Ensemble Learning","display_name":"Overcoming Training Data Scarcity in Routing Demand Prediction via Ensemble Learning","publication_year":2025,"publication_date":"2025-06-27","ids":{"openalex":"https://openalex.org/W4411713434","doi":"https://doi.org/10.1145/3716368.3735237"},"language":"en","primary_location":{"id":"doi:10.1145/3716368.3735237","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3716368.3735237","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Great Lakes Symposium on VLSI 2025","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":"Yu-Guang Chen","orcid":"https://orcid.org/0000-0002-9616-7379"},"institutions":[{"id":"https://openalex.org/I22265921","display_name":"National Central University","ror":"https://ror.org/00944ve71","country_code":"TW","type":"education","lineage":["https://openalex.org/I22265921"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yu-Guang Chen","raw_affiliation_strings":["Department of Electrical Engineering, National Central University, Taoyuan, Taiwan, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-9616-7379","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Central University, Taoyuan, Taiwan, Taiwan","institution_ids":["https://openalex.org/I22265921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111910815","display_name":"Shyh-Jier Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210086231","display_name":"Global Unichip (Taiwan)","ror":"https://ror.org/00005jn19","country_code":"TW","type":"company","lineage":["https://openalex.org/I4210086231"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Shih-Cheng Huang","raw_affiliation_strings":["Global Unichip Corporation, Hsinchu, Taiwan, Taiwan"],"raw_orcid":"https://orcid.org/0009-0004-7894-1668","affiliations":[{"raw_affiliation_string":"Global Unichip Corporation, Hsinchu, Taiwan, Taiwan","institution_ids":["https://openalex.org/I4210086231"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019183410","display_name":"Cheng-Hong Tsai","orcid":"https://orcid.org/0009-0001-6719-6728"},"institutions":[{"id":"https://openalex.org/I4210086231","display_name":"Global Unichip (Taiwan)","ror":"https://ror.org/00005jn19","country_code":"TW","type":"company","lineage":["https://openalex.org/I4210086231"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Cheng-Hong Tsai","raw_affiliation_strings":["Global Unichip Corporation, Hsinchu, Taiwan, Taiwan"],"raw_orcid":"https://orcid.org/0009-0001-6719-6728","affiliations":[{"raw_affiliation_string":"Global Unichip Corporation, Hsinchu, Taiwan, Taiwan","institution_ids":["https://openalex.org/I4210086231"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042656310","display_name":"De-Shiun Fu","orcid":"https://orcid.org/0009-0006-4333-5355"},"institutions":[{"id":"https://openalex.org/I4210086231","display_name":"Global Unichip (Taiwan)","ror":"https://ror.org/00005jn19","country_code":"TW","type":"company","lineage":["https://openalex.org/I4210086231"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"De-Shiun Fu","raw_affiliation_strings":["Global Unichip Corporation, Hsinchu, Taiwan, Taiwan"],"raw_orcid":"https://orcid.org/0009-0006-4333-5355","affiliations":[{"raw_affiliation_string":"Global Unichip Corporation, Hsinchu, Taiwan, Taiwan","institution_ids":["https://openalex.org/I4210086231"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036562820","display_name":"Mango Chia-Tso Chao","orcid":"https://orcid.org/0009-0006-5842-8546"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Mango Chia-Tso Chao","raw_affiliation_strings":["Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, Taiwan"],"raw_orcid":"https://orcid.org/0009-0006-5842-8546","affiliations":[{"raw_affiliation_string":"Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"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":0.14040521,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"681","last_page":"688"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9876999855041504,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6958287358283997},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6911446452140808},{"id":"https://openalex.org/keywords/scarcity","display_name":"Scarcity","score":0.6636531949043274},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5264304876327515},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.49772265553474426},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4230371415615082},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4222296476364136},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.41865676641464233},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.1290186643600464},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.11440476775169373},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.07903534173965454},{"id":"https://openalex.org/keywords/microeconomics","display_name":"Microeconomics","score":0.07682842016220093},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06133154034614563}],"concepts":[{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6958287358283997},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6911446452140808},{"id":"https://openalex.org/C109747225","wikidata":"https://www.wikidata.org/wiki/Q815758","display_name":"Scarcity","level":2,"score":0.6636531949043274},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5264304876327515},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.49772265553474426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4230371415615082},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4222296476364136},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.41865676641464233},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.1290186643600464},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.11440476775169373},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.07903534173965454},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.07682842016220093},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06133154034614563}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3716368.3735237","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3716368.3735237","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Great Lakes Symposium on VLSI 2025","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.4099999964237213}],"awards":[{"id":"https://openalex.org/G3305159976","display_name":null,"funder_award_id":"112-2221-E-008 -097-MY3, 113-2218-E-007-020, 113-2640-E-008-001, 113-2640-E-006-001, 114-2218-E-007-002","funder_id":"https://openalex.org/F2461203286","funder_display_name":"National Science and Technology Council"}],"funders":[{"id":"https://openalex.org/F2461203286","display_name":"National Science and Technology Council","ror":"https://ror.org/02kv4zf79"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2161826494","https://openalex.org/W2295598076","https://openalex.org/W2899885603","https://openalex.org/W2945706499","https://openalex.org/W2972606367","https://openalex.org/W3036947539","https://openalex.org/W3042395213","https://openalex.org/W3176708584","https://openalex.org/W3185136323","https://openalex.org/W3186888684","https://openalex.org/W3210573375","https://openalex.org/W4200348341","https://openalex.org/W4212862653","https://openalex.org/W4238447347","https://openalex.org/W4378800969"],"related_works":["https://openalex.org/W1571141552","https://openalex.org/W4391636338","https://openalex.org/W4386067343","https://openalex.org/W4294250823","https://openalex.org/W2093086151","https://openalex.org/W55936454","https://openalex.org/W2095572632","https://openalex.org/W230091440","https://openalex.org/W4394050964","https://openalex.org/W2551249631"],"abstract_inverted_index":{"As":[0],"CMOS":[1],"technology":[2],"scales":[3],"down,":[4],"the":[5,17,26,31,56],"number":[6],"of":[7,19,25,68,125],"standard":[8],"cells":[9],"increases":[10],"rapidly.":[11],"The":[12,92,110],"increasing":[13],"cell":[14],"count":[15],"raises":[16],"complexity":[18],"physical":[20,32],"design.":[21],"Routing":[22],"is":[23,82,113],"one":[24],"most":[27],"time-consuming":[28],"stages":[29,49],"in":[30],"design":[33,40,61,73],"flow.":[34],"When":[35],"routing":[36,57,69,87],"fails":[37],"to":[38,84,118],"meet":[39],"rules":[41],"or":[42,53],"performance":[43],"targets,":[44],"designers":[45],"must":[46],"revise":[47],"earlier":[48],"such":[50],"as":[51],"floorplanning":[52],"placement.":[54],"Repeating":[55],"process":[58],"causes":[59],"high":[60],"cost":[62],"and":[63,100,107,127],"long":[64],"time-to-market.":[65],"Early":[66],"prediction":[67],"demand":[70,88],"helps":[71],"reduce":[72],"iterations.":[74],"An":[75],"ensemble":[76],"learning":[77],"model":[78,94],"based":[79],"on":[80],"XGBoost":[81],"proposed":[83],"predict":[85],"global":[86],"using":[89],"placement-stage":[90],"features.":[91],"XGBoost-based":[93],"achieves":[95],"higher":[96],"accuracy":[97,132],"than":[98],"CNN-":[99],"FCN-based":[101],"models,":[102],"improving":[103],"R\u00b2":[104],"by":[105],"0.12":[106],"0.125,":[108],"respectively.":[109],"inference":[111,128],"speed":[112],"also":[114],"significantly":[115],"faster,":[116],"up":[117],"14.95\u00d7.":[119],"Feature":[120],"importance":[121],"analysis":[122],"enables":[123],"reduction":[124],"training":[126],"overhead":[129],"with":[130],"minimal":[131],"loss.":[133]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
