{"id":"https://openalex.org/W4393140724","doi":"https://doi.org/10.1109/asp-dac58780.2024.10473883","title":"SparGNN: Efficient Joint Feature-Model Sparsity Exploitation in Graph Neural Network Acceleration","display_name":"SparGNN: Efficient Joint Feature-Model Sparsity Exploitation in Graph Neural Network Acceleration","publication_year":2024,"publication_date":"2024-01-22","ids":{"openalex":"https://openalex.org/W4393140724","doi":"https://doi.org/10.1109/asp-dac58780.2024.10473883"},"language":"en","primary_location":{"id":"doi:10.1109/asp-dac58780.2024.10473883","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/asp-dac58780.2024.10473883","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC)","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/A5012267222","display_name":"Chen Yin","orcid":"https://orcid.org/0000-0001-9112-1251"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Yin","raw_affiliation_strings":["Shanghai Jiao Tong University,Department of Micro-Nano Electronics,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Department of Micro-Nano Electronics,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008057183","display_name":"Jianfei Jiang","orcid":"https://orcid.org/0000-0002-5521-6197"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianfei Jiang","raw_affiliation_strings":["Shanghai Jiao Tong University,Department of Micro-Nano Electronics,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Department of Micro-Nano Electronics,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100422823","display_name":"Qin Wang","orcid":"https://orcid.org/0000-0003-2264-749X"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qin Wang","raw_affiliation_strings":["Shanghai Jiao Tong University,Department of Micro-Nano Electronics,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Department of Micro-Nano Electronics,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103236320","display_name":"Zhigang Mao","orcid":"https://orcid.org/0000-0001-9431-9853"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhigang Mao","raw_affiliation_strings":["Shanghai Jiao Tong University,Department of Micro-Nano Electronics,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Department of Micro-Nano Electronics,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045693138","display_name":"Naifeng Jing","orcid":"https://orcid.org/0000-0001-8417-5796"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Naifeng Jing","raw_affiliation_strings":["Shanghai Jiao Tong University,Department of Micro-Nano Electronics,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Department of Micro-Nano Electronics,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"225","last_page":"230"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9850000143051147,"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":0.9850000143051147,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9631999731063843,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9189000129699707,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7722902297973633},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.6289221048355103},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5953627228736877},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.5939128994941711},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.535454273223877},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48048272728919983},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4634324014186859},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3903333246707916},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.292391836643219},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.0798199474811554}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7722902297973633},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.6289221048355103},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5953627228736877},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.5939128994941711},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.535454273223877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48048272728919983},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4634324014186859},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3903333246707916},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.292391836643219},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0798199474811554},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/asp-dac58780.2024.10473883","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/asp-dac58780.2024.10473883","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC)","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":16,"referenced_works":["https://openalex.org/W2055935910","https://openalex.org/W3006586535","https://openalex.org/W3017228913","https://openalex.org/W3103168911","https://openalex.org/W3105753905","https://openalex.org/W3132616766","https://openalex.org/W3152893301","https://openalex.org/W3206743063","https://openalex.org/W4281800613","https://openalex.org/W4308083753","https://openalex.org/W4318328413","https://openalex.org/W4360831816","https://openalex.org/W4360831960","https://openalex.org/W4360831975","https://openalex.org/W4388878526","https://openalex.org/W6790791390"],"related_works":["https://openalex.org/W2390829436","https://openalex.org/W1989791859","https://openalex.org/W2565094479","https://openalex.org/W602859758","https://openalex.org/W2006439817","https://openalex.org/W1971289376","https://openalex.org/W2379101322","https://openalex.org/W1992553864","https://openalex.org/W2776207444","https://openalex.org/W2726447019"],"abstract_inverted_index":{"With":[0],"the":[1,62,102,114,124,143,179],"rapid":[2],"explosion":[3],"in":[4,35,44,85,128],"both":[5,86,129],"graph":[6,12,117],"scale":[7,17],"and":[8,23,56,76,88,131,190],"model":[9],"size,":[10],"accelerating":[11],"neural":[13,37],"networks":[14,38],"(GNNs)":[15],"at":[16],"encounters":[18],"significant":[19],"pressure":[20],"on":[21],"computation":[22,189],"memory":[24],"footprint.":[25],"Exploiting":[26],"data":[27,83,126,140],"sparsity":[28,84,127],"with":[29,199],"pruning,":[30,106,119],"which":[31,120],"exhibits":[32],"remarkable":[33],"effect":[34],"deep":[36],"(DNNs),":[39],"while":[40,94],"still":[41],"lags":[42],"behind":[43],"GNN":[45,65,92,170,181],"acceleration.":[46],"This":[47],"is":[48],"because":[49],"costly":[50],"pruning":[51],"overhead":[52,103],"upon":[53],"large":[54],"graphs":[55],"inefficient":[57],"hardware":[58,161],"support":[59],"will":[60],"eclipse":[61],"benefit":[63],"of":[64,104,142,194],"sparsification.":[66],"To":[67],"this":[68,70],"end,":[69],"paper":[71],"proposes":[72],"SparGNN,":[73],"an":[74,192],"algorithm":[75],"accelerator":[77],"co-design":[78],"that":[79,176],"can":[80,121],"efficiently":[81],"exploit":[82],"features":[87,130],"models":[89,132],"to":[90,100,112,138,163,168,178],"speedup":[91,198],"acceleration":[93],"reserving":[95],"its":[96],"accuracy.":[97],"In":[98,136],"algorithm,":[99],"reduce":[101],"iterative":[105],"we":[107,147],"distill":[108],"a":[109],"sparsified":[110,144],"subgraph":[111],"substitute":[113],"original":[115],"input":[116],"for":[118,153],"low-costly":[122],"excavate":[123],"potential":[125],"without":[133],"accuracy":[134],"compromise.":[135],"hardware,":[137],"improve":[139],"locality":[141],"feature-weight":[145],"multiplication,":[146],"design":[148,166],"compressed":[149],"row-/column-wise":[150],"product":[151],"dataflow":[152],"efficient":[154],"feature":[155],"updating.":[156],"We":[157],"then":[158],"propose":[159],"lightweight":[160],"changes":[162],"make":[164],"our":[165],"applicable":[167],"conventional":[169],"accelerators.":[171],"The":[172],"experimental":[173],"results":[174],"show":[175],"compared":[177],"state-of-the-art":[180],"accelerators,":[182],"SparGNN":[183],"reduces":[184],"$1.5":[185],"\\sim":[186,201],"4.3":[187],"\\times$":[188,203],"gains":[191],"average":[193],"1.8":[195],"6.8":[196],"$\\times$":[197],"$1.4":[200],"9.2":[202],"energy":[204],"efficiency":[205],"improvement.":[206]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}