{"id":"https://openalex.org/W3173701743","doi":"https://doi.org/10.1145/3453688.3461741","title":"Co-Exploration of Graph Neural Network and Network-on-Chip Design Using AutoML","display_name":"Co-Exploration of Graph Neural Network and Network-on-Chip Design Using AutoML","publication_year":2021,"publication_date":"2021-06-18","ids":{"openalex":"https://openalex.org/W3173701743","doi":"https://doi.org/10.1145/3453688.3461741","mag":"3173701743"},"language":"en","primary_location":{"id":"doi:10.1145/3453688.3461741","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3453688.3461741","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 Great Lakes Symposium on VLSI","raw_type":"proceedings-article"},"type":"conference-paper","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/A5050337587","display_name":"Daniel Manu","orcid":"https://orcid.org/0000-0001-5498-2677"},"institutions":[{"id":"https://openalex.org/I169521973","display_name":"University of New Mexico","ror":"https://ror.org/05fs6jp91","country_code":"US","type":"education","lineage":["https://openalex.org/I169521973"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Manu","raw_affiliation_strings":["University of New Mexico, Albuquerque, NM, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of New Mexico, Albuquerque, NM, USA","institution_ids":["https://openalex.org/I169521973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073345631","display_name":"Shaoyi Huang","orcid":"https://orcid.org/0000-0001-6093-9798"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shaoyi Huang","raw_affiliation_strings":["University of Connecticut, Storrs, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030060072","display_name":"Caiwen Ding","orcid":"https://orcid.org/0000-0003-0891-1231"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Caiwen Ding","raw_affiliation_strings":["University of Connecticut, Storrs, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Connecticut, Storrs, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100698152","display_name":"Lei Yang","orcid":"https://orcid.org/0000-0002-0646-440X"},"institutions":[{"id":"https://openalex.org/I169521973","display_name":"University of New Mexico","ror":"https://ror.org/05fs6jp91","country_code":"US","type":"education","lineage":["https://openalex.org/I169521973"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lei Yang","raw_affiliation_strings":["University of New Mexico, Albuquerque, NM, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of New Mexico, Albuquerque, NM, USA","institution_ids":["https://openalex.org/I169521973"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"175","last_page":"180"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9986000061035156,"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":0.9986000061035156,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9896000027656555,"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/T10083","display_name":"Graphene research and applications","score":0.986299991607666,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials 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":0.8009644150733948},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.6427515745162964},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5926467776298523},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5593182444572449},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5305697321891785},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.4976976215839386},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.4507734775543213},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39641404151916504},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3376706838607788},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.33246850967407227},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.28691208362579346},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.24120599031448364}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8009644150733948},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.6427515745162964},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5926467776298523},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5593182444572449},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5305697321891785},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.4976976215839386},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.4507734775543213},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39641404151916504},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3376706838607788},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.33246850967407227},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28691208362579346},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.24120599031448364},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3453688.3461741","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3453688.3461741","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 Great Lakes Symposium on VLSI","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W626972714","https://openalex.org/W1501856433","https://openalex.org/W2116341502","https://openalex.org/W2315403234","https://openalex.org/W2318454936","https://openalex.org/W2468907370","https://openalex.org/W2809418595","https://openalex.org/W2895195713","https://openalex.org/W2911884654","https://openalex.org/W2917450576","https://openalex.org/W2949208225","https://openalex.org/W2974152075","https://openalex.org/W3016142271","https://openalex.org/W3017228913","https://openalex.org/W3046314183","https://openalex.org/W3090369187","https://openalex.org/W3091602279","https://openalex.org/W3093649180","https://openalex.org/W3102800594","https://openalex.org/W3105136071","https://openalex.org/W6600120041","https://openalex.org/W6601955380"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W2811106690","https://openalex.org/W4239306820","https://openalex.org/W2947043951","https://openalex.org/W2318112981","https://openalex.org/W4312417841","https://openalex.org/W4210874298","https://openalex.org/W2038503502"],"abstract_inverted_index":{"Recently,":[0],"Graph":[1],"Neural":[2,28],"Networks":[3],"(GNNs)":[4],"have":[5,31],"exhibited":[6],"high":[7],"efficiency":[8],"in":[9,51],"several":[10],"graph-based":[11],"machine":[12],"learning":[13],"tasks.":[14],"Compared":[15],"with":[16,65],"the":[17,39,60,66,69,73,93,101,114,120],"neural":[18],"networks":[19],"for":[20,47,90],"computer":[21],"vision":[22],"or":[23],"speech":[24],"tasks":[25],"(e.g.,":[26],"Convolutional":[27],"Networks),":[29],"GNNs":[30,46],"much":[32],"higher":[33],"requirements":[34],"on":[35],"communication":[36],"due":[37],"to":[38,84,104],"complicated":[40,70],"graph":[41],"structures;":[42],"however,":[43],"when":[44],"applying":[45],"real-world":[48],"applications,":[49],"say":[50],"recommender":[52],"systems":[53],"(e.g.":[54],"Uber":[55],"Eats),":[56],"it":[57],"commonly":[58],"has":[59],"real-time":[61],"requirements.":[62],"To":[63],"deal":[64],"tradeoff":[67],"between":[68,113],"architecture":[71,79,118],"and":[72,80,119],"high-demand":[74],"timing":[75],"performance,":[76],"both":[77],"GNN":[78,117],"hardware":[81],"accelerator":[82],"need":[83],"be":[85],"optimized.":[86],"Network-on-Chip":[87],"(NoC),":[88],"derived":[89],"efficiently":[91],"managing":[92],"high-volume":[94],"of":[95,100,116],"communications,":[96],"naturally":[97],"becomes":[98],"one":[99],"top":[102],"candidates":[103],"accelerate":[105],"GNNs.":[106],"However,":[107],"there":[108],"is":[109],"a":[110],"missing":[111],"link":[112],"optimize":[115],"NoC":[121],"design.":[122]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2022,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
