{"id":"https://openalex.org/W3200832253","doi":"https://doi.org/10.1109/iccad51958.2021.9643549","title":"G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency","display_name":"G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency","publication_year":2021,"publication_date":"2021-11-01","ids":{"openalex":"https://openalex.org/W3200832253","doi":"https://doi.org/10.1109/iccad51958.2021.9643549","mag":"3200832253"},"language":"en","primary_location":{"id":"doi:10.1109/iccad51958.2021.9643549","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccad51958.2021.9643549","pdf_url":null,"source":{"id":"https://openalex.org/S4363608354","display_name":"2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)","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":"2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)","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/A5101912940","display_name":"Yong-An Zhang","orcid":"https://orcid.org/0000-0002-1118-7072"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yongan Zhang","raw_affiliation_strings":["Rice University, Houston, TX"],"affiliations":[{"raw_affiliation_string":"Rice University, Houston, TX","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080793133","display_name":"Haoran You","orcid":null},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haoran You","raw_affiliation_strings":["Rice University, Houston, TX"],"affiliations":[{"raw_affiliation_string":"Rice University, Houston, TX","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061572132","display_name":"Yonggan Fu","orcid":"https://orcid.org/0000-0002-7483-2921"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yonggan Fu","raw_affiliation_strings":["Rice University, Houston, TX"],"affiliations":[{"raw_affiliation_string":"Rice University, Houston, TX","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078443672","display_name":"Tong Geng","orcid":"https://orcid.org/0000-0002-3644-2922"},"institutions":[{"id":"https://openalex.org/I142606810","display_name":"Pacific Northwest National Laboratory","ror":"https://ror.org/05h992307","country_code":"US","type":"facility","lineage":["https://openalex.org/I1325736334","https://openalex.org/I1330989302","https://openalex.org/I142606810","https://openalex.org/I39565521"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tong Geng","raw_affiliation_strings":["Pacific Northwest National Laboratory, Richland, WA"],"affiliations":[{"raw_affiliation_string":"Pacific Northwest National Laboratory, Richland, WA","institution_ids":["https://openalex.org/I142606810"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100413662","display_name":"Ang Li","orcid":"https://orcid.org/0000-0003-4472-4919"},"institutions":[{"id":"https://openalex.org/I142606810","display_name":"Pacific Northwest National Laboratory","ror":"https://ror.org/05h992307","country_code":"US","type":"facility","lineage":["https://openalex.org/I1325736334","https://openalex.org/I1330989302","https://openalex.org/I142606810","https://openalex.org/I39565521"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ang Li","raw_affiliation_strings":["Pacific Northwest National Laboratory, Richland, WA"],"affiliations":[{"raw_affiliation_string":"Pacific Northwest National Laboratory, Richland, WA","institution_ids":["https://openalex.org/I142606810"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019582323","display_name":"Yingyan Lin","orcid":"https://orcid.org/0000-0001-5946-203X"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yingyan Lin","raw_affiliation_strings":["Rice University, Houston, TX"],"affiliations":[{"raw_affiliation_string":"Rice University, Houston, TX","institution_ids":["https://openalex.org/I74775410"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101912940"],"corresponding_institution_ids":["https://openalex.org/I74775410"],"apc_list":null,"apc_paid":null,"fwci":2.0105,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.89437278,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":1.0,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.9939000010490417,"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/T10028","display_name":"Topic Modeling","score":0.987500011920929,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7458089590072632},{"id":"https://openalex.org/keywords/expediting","display_name":"Expediting","score":0.6445682048797607},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5967990159988403},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.5702514052391052},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5663902759552002},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4683885872364044},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.387782484292984},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37675750255584717},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2698177695274353},{"id":"https://openalex.org/keywords/systems-engineering","display_name":"Systems engineering","score":0.0811273455619812}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7458089590072632},{"id":"https://openalex.org/C134448949","wikidata":"https://www.wikidata.org/wiki/Q1384274","display_name":"Expediting","level":2,"score":0.6445682048797607},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5967990159988403},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.5702514052391052},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5663902759552002},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4683885872364044},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.387782484292984},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37675750255584717},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2698177695274353},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0811273455619812},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccad51958.2021.9643549","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccad51958.2021.9643549","pdf_url":null,"source":{"id":"https://openalex.org/S4363608354","display_name":"2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)","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":"2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1037327305","display_name":null,"funder_award_id":"R01HL144683","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G450053395","display_name":null,"funder_award_id":"CAREER-2048183","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":80,"referenced_works":["https://openalex.org/W1501856433","https://openalex.org/W1522301498","https://openalex.org/W2153959628","https://openalex.org/W2553303224","https://openalex.org/W2606202972","https://openalex.org/W2619307294","https://openalex.org/W2624431344","https://openalex.org/W2766362889","https://openalex.org/W2788919350","https://openalex.org/W2807021761","https://openalex.org/W2809418595","https://openalex.org/W2898565872","https://openalex.org/W2918342466","https://openalex.org/W2940562175","https://openalex.org/W2946948417","https://openalex.org/W2951104886","https://openalex.org/W2955425717","https://openalex.org/W2957020430","https://openalex.org/W2962711740","https://openalex.org/W2962716258","https://openalex.org/W2962746461","https://openalex.org/W2962767366","https://openalex.org/W2962856739","https://openalex.org/W2963858333","https://openalex.org/W2963918968","https://openalex.org/W2964015378","https://openalex.org/W2967733054","https://openalex.org/W2971564650","https://openalex.org/W2982083293","https://openalex.org/W2996835428","https://openalex.org/W2997079913","https://openalex.org/W2998003319","https://openalex.org/W3005828390","https://openalex.org/W3016142271","https://openalex.org/W3017228913","https://openalex.org/W3021747854","https://openalex.org/W3023151209","https://openalex.org/W3023357481","https://openalex.org/W3034609471","https://openalex.org/W3043495022","https://openalex.org/W3045613009","https://openalex.org/W3047846843","https://openalex.org/W3091862797","https://openalex.org/W3092020069","https://openalex.org/W3092216346","https://openalex.org/W3096533519","https://openalex.org/W3098230582","https://openalex.org/W3100078588","https://openalex.org/W3100848837","https://openalex.org/W3105136071","https://openalex.org/W3105753905","https://openalex.org/W3106539141","https://openalex.org/W3111579839","https://openalex.org/W3127829048","https://openalex.org/W3137187143","https://openalex.org/W3155247222","https://openalex.org/W3158126894","https://openalex.org/W3167905203","https://openalex.org/W3191594941","https://openalex.org/W3209992885","https://openalex.org/W4210257598","https://openalex.org/W4288419263","https://openalex.org/W4294558607","https://openalex.org/W4297733535","https://openalex.org/W4302334595","https://openalex.org/W6726873649","https://openalex.org/W6738693370","https://openalex.org/W6738964360","https://openalex.org/W6748524405","https://openalex.org/W6754929296","https://openalex.org/W6760045743","https://openalex.org/W6766225098","https://openalex.org/W6767959926","https://openalex.org/W6772384842","https://openalex.org/W6774478992","https://openalex.org/W6781376665","https://openalex.org/W6785059380","https://openalex.org/W6796027562","https://openalex.org/W6803870796","https://openalex.org/W6807384801"],"related_works":["https://openalex.org/W2024632604","https://openalex.org/W2085395339","https://openalex.org/W2071999521","https://openalex.org/W2068005943","https://openalex.org/W2010478499","https://openalex.org/W4317792299","https://openalex.org/W2050011582","https://openalex.org/W2071715249","https://openalex.org/W4300465923","https://openalex.org/W2145423738"],"abstract_inverted_index":{"Graph":[0],"Neural":[1],"Networks":[2],"(GNNs)":[3],"have":[4],"emerged":[5],"as":[6],"the":[7,52,61,147,154,170,200,204],"state-of-the-art":[8],"(SOTA)":[9],"method":[10],"for":[11,88,138,158,199],"graph-based":[12],"learning":[13],"tasks.":[14,34],"However,":[15],"it":[16,55],"still":[17,57],"remains":[18],"prohibitively":[19],"challenging":[20],"to":[21,31,60,94,119],"inference":[22,48],"GNNs":[23,39,68,159,171,180,207],"over":[24],"large":[25],"graph":[26],"datasets,":[27],"limiting":[28],"their":[29,41,70,143,161,209],"application":[30],"large-scale":[32],"real-world":[33],"While":[35],"end-to-end":[36,201],"jointly":[37],"optimizing":[38],"and":[40,50,63,69,80,92,99,123,128,135,142,160,165,172,181,190,208],"accelerators":[42,93,173,183],"is":[43,56,117,153],"promising":[44],"in":[45,184],"boosting":[46],"GNNs'":[47],"efficiency":[49],"expediting":[51],"design":[53,65],"process,":[54],"underexplored":[58],"due":[59],"vast":[62],"distinct":[64],"spaces":[66],"of":[67,149,186,203],"accelerators.":[71,145,162,210],"In":[72],"this":[73],"work,":[74],"we":[75],"propose":[76],"G-CoS,":[77],"a":[78,110,125,196],"GNN":[79,90,112,121,127,140,182],"accelerator":[81,113,129],"co-search":[82,130,156],"framework":[83,157],"that":[84,132,169],"can":[85],"automatically":[86],"search":[87,114,137],"matched":[89,144,206],"structures":[91,122,141],"maximize":[95],"both":[96,187],"task":[97,188],"accuracy":[98,189],"acceleration":[100],"efficiency.":[101],"Specifically,":[102],"G-CoS":[103,152,176],"integrates":[104],"two":[105],"major":[106],"enabling":[107],"components:":[108],"(1)":[109],"generic":[111],"space":[115],"which":[116],"applicable":[118],"various":[120],"(2)":[124],"one-shot":[126],"algorithm":[131],"enables":[133],"simultaneous":[134],"efficient":[136],"optimal":[139],"To":[146],"best":[148,205],"our":[150],"knowledge,":[151],"first":[155],"Extensive":[163],"experiments":[164],"ablation":[166],"studies":[167],"show":[168],"generated":[174],"by":[175],"consistently":[177],"outperform":[178],"SOTA":[179],"terms":[185],"hardware":[191],"efficiency,":[192],"while":[193],"only":[194],"requiring":[195],"few":[197],"hours":[198],"generation":[202]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
