{"id":"https://openalex.org/W4386227260","doi":"https://doi.org/10.1145/3589737.3605983","title":"Efficient GCN Deployment with Spiking Property on Spatial-Temporal Neuromorphic Chips","display_name":"Efficient GCN Deployment with Spiking Property on Spatial-Temporal Neuromorphic Chips","publication_year":2023,"publication_date":"2023-08-01","ids":{"openalex":"https://openalex.org/W4386227260","doi":"https://doi.org/10.1145/3589737.3605983"},"language":"en","primary_location":{"id":"doi:10.1145/3589737.3605983","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1145/3589737.3605983","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589737.3605983","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Neuromorphic Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3589737.3605983","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030937860","display_name":"Hongyi Li","orcid":"https://orcid.org/0000-0002-7495-9930"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hongyi Li","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030409594","display_name":"Mingkun Xu","orcid":"https://orcid.org/0000-0003-4329-8735"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingkun Xu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017541431","display_name":"Jing Pei","orcid":"https://orcid.org/0000-0003-2340-0616"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Pei","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100675943","display_name":"Rong Zhao","orcid":"https://orcid.org/0000-0002-2320-0326"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rong Zhao","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5030937860"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.1337,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.43748073,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9995999932289124,"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/T12236","display_name":"Photoreceptor and optogenetics research","score":0.980400025844574,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.8462406396865845},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8103711009025574},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.6180582642555237},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5607835650444031},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5463616251945496},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.5266444683074951},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.510099470615387},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.4624502956867218},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4454270601272583},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.42996177077293396},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.37904635071754456},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.37846583127975464},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36744382977485657},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.3569948673248291},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.29790598154067993},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1445036232471466}],"concepts":[{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.8462406396865845},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8103711009025574},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.6180582642555237},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5607835650444031},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5463616251945496},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.5266444683074951},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.510099470615387},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.4624502956867218},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4454270601272583},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.42996177077293396},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.37904635071754456},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.37846583127975464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36744382977485657},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.3569948673248291},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29790598154067993},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1445036232471466},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3589737.3605983","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1145/3589737.3605983","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589737.3605983","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Neuromorphic Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3589737.3605983","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1145/3589737.3605983","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589737.3605983","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Neuromorphic Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386227260.pdf","grobid_xml":"https://content.openalex.org/works/W4386227260.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W1604973310","https://openalex.org/W2090066236","https://openalex.org/W2153959628","https://openalex.org/W2605487586","https://openalex.org/W2783525259","https://openalex.org/W2966081953","https://openalex.org/W2985331920","https://openalex.org/W3017521908","https://openalex.org/W3131444756","https://openalex.org/W3174155187","https://openalex.org/W3183104940","https://openalex.org/W3191283164","https://openalex.org/W4282837277","https://openalex.org/W4282927077","https://openalex.org/W4321484024","https://openalex.org/W4327911464","https://openalex.org/W4362607240","https://openalex.org/W4388469761","https://openalex.org/W6682573843"],"related_works":["https://openalex.org/W3089892344","https://openalex.org/W2960220682","https://openalex.org/W4313442939","https://openalex.org/W4386227293","https://openalex.org/W4372267706","https://openalex.org/W2885510266","https://openalex.org/W4288055417","https://openalex.org/W4205804651","https://openalex.org/W4287758233","https://openalex.org/W3136467750"],"abstract_inverted_index":{"Spiking":[0],"Graph":[1],"Convolutional":[2],"Networks":[3],"(SGCNs)":[4],"are":[5],"an":[6,67],"emerging":[7],"class":[8],"of":[9,21,92,105,149],"neural":[10,24],"networks":[11,25],"that":[12,125],"operate":[13],"on":[14,39,59,131],"graph-structured":[15],"data":[16],"and":[17,26,52,80,95,111,119,143],"leverage":[18],"the":[19,89,93,103],"advantages":[20],"both":[22],"spiking":[23,47],"GCNs,":[27],"making":[28],"them":[29],"ideal":[30],"for":[31],"graph":[32],"representation":[33],"learning":[34],"tasks.":[35],"However,":[36],"deploying":[37],"SGCNs":[38,79],"neuromorphic":[40,74],"chips":[41],"is":[42],"challenging":[43],"due":[44],"to":[45,76,139],"complex":[46],"dynamics,":[48],"structured":[49],"message":[50],"computation,":[51],"limited":[53],"chip":[54,75,82,94],"resources,":[55],"impeding":[56],"their":[57],"applications":[58],"terminal":[60],"devices.":[61],"In":[62],"this":[63],"work,":[64],"we":[65],"present":[66],"algorithm":[68],"mapping":[69,86],"strategy":[70],"using":[71],"a":[72,109,115],"contemporary":[73],"efficiently":[77],"deploy":[78],"maximize":[81],"resource":[83],"utilization.":[84],"Our":[85],"scheme":[87,117],"leverages":[88],"spatio-temporal":[90],"features":[91],"partitions":[96],"computation":[97,106],"graphs":[98],"into":[99],"small":[100],"sub-graphs,":[101],"enabling":[102],"reuse":[104],"resources":[107],"in":[108],"temporal":[110],"spatial":[112],"manner":[113],"by":[114,135],"flexible":[116],"setting":[118],"corresponding":[120],"scheduling.":[121],"Simulation":[122],"experiments":[123],"show":[124],"our":[126],"approach":[127],"achieves":[128],"favorable":[129],"results":[130],"citation":[132],"benchmark":[133],"datasets":[134],"significantly":[136],"reducing":[137],"up":[138],"79.8%":[140],"memory":[141],"usage":[142],"more":[144],"than":[145],"60%":[146],"computational":[147],"requirements":[148],"SGCN":[150],"deployment.":[151]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-24T23:09:58.560324","created_date":"2025-10-10T00:00:00"}