{"id":"https://openalex.org/W4402351831","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651169","title":"OneSpike: Ultra-low latency spiking neural networks","display_name":"OneSpike: Ultra-low latency spiking neural networks","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402351831","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651169"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10651169","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651169","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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/A5113125453","display_name":"Kaiwen Tang","orcid":"https://orcid.org/0009-0008-7765-2505"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Kaiwen Tang","raw_affiliation_strings":["National University of Singapore,School of Computing,Singapore,Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Singapore,School of Computing,Singapore,Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004250652","display_name":"Zhanglu Yan","orcid":"https://orcid.org/0000-0001-7993-7127"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Zhanglu Yan","raw_affiliation_strings":["National University of Singapore,School of Computing,Singapore,Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Singapore,School of Computing,Singapore,Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023989495","display_name":"Weng\u2010Fai Wong","orcid":"https://orcid.org/0000-0002-4281-2053"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Weng-Fai Wong","raw_affiliation_strings":["National University of Singapore,School of Computing,Singapore,Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Singapore,School of Computing,Singapore,Singapore","institution_ids":["https://openalex.org/I165932596"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I165932596"],"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":"34","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/T10320","display_name":"Neural Networks and Applications","score":0.9980999827384949,"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/T10581","display_name":"Neural dynamics and brain function","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive 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/spiking-neural-network","display_name":"Spiking neural network","score":0.7535876035690308},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6859477758407593},{"id":"https://openalex.org/keywords/low-latency","display_name":"Low latency (capital markets)","score":0.5547665953636169},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5101706981658936},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.4951874315738678},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.2808079719543457},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2336653769016266},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.16660082340240479}],"concepts":[{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.7535876035690308},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6859477758407593},{"id":"https://openalex.org/C46637626","wikidata":"https://www.wikidata.org/wiki/Q6693015","display_name":"Low latency (capital markets)","level":2,"score":0.5547665953636169},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5101706981658936},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.4951874315738678},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.2808079719543457},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2336653769016266},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.16660082340240479}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10651169","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651169","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1645800954","https://openalex.org/W1686810756","https://openalex.org/W1999085092","https://openalex.org/W2098366275","https://openalex.org/W2108598243","https://openalex.org/W2127712540","https://openalex.org/W2319920447","https://openalex.org/W2562377059","https://openalex.org/W2887447938","https://openalex.org/W2981819390","https://openalex.org/W3038819247","https://openalex.org/W3097140984","https://openalex.org/W3102750118","https://openalex.org/W3118608800","https://openalex.org/W3167976421","https://openalex.org/W3170540448","https://openalex.org/W3175674467","https://openalex.org/W3207598347","https://openalex.org/W3208650852","https://openalex.org/W3213590062","https://openalex.org/W3214982345","https://openalex.org/W4280539093","https://openalex.org/W4281708609","https://openalex.org/W4287824105","https://openalex.org/W4295312788","https://openalex.org/W4298323717","https://openalex.org/W4300980424","https://openalex.org/W4301188113","https://openalex.org/W4309217996","https://openalex.org/W4312383953","https://openalex.org/W4323708667","https://openalex.org/W4367000142","https://openalex.org/W4385965686","https://openalex.org/W4390647507","https://openalex.org/W4392902786","https://openalex.org/W6637373629","https://openalex.org/W6700264148","https://openalex.org/W6730823560","https://openalex.org/W6766978945","https://openalex.org/W6772326514","https://openalex.org/W6776469097","https://openalex.org/W6787972765","https://openalex.org/W6796421320","https://openalex.org/W6796939779","https://openalex.org/W6802059322","https://openalex.org/W6803524348","https://openalex.org/W6838683425","https://openalex.org/W6844652965","https://openalex.org/W6847893317","https://openalex.org/W6850626707","https://openalex.org/W6852107617","https://openalex.org/W6856121576","https://openalex.org/W6860144444"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W3205411230","https://openalex.org/W4286899009","https://openalex.org/W9168048","https://openalex.org/W4300849822","https://openalex.org/W4376480820","https://openalex.org/W3155891479","https://openalex.org/W3029351463","https://openalex.org/W4308600690"],"abstract_inverted_index":{"With":[0],"the":[1,35,46,61,64,69,86,105,118,162,172,180,197,204,211],"development":[2,33],"of":[3,37,48,63,88,159,174,199],"deep":[4],"learning":[5],"models,":[6],"there":[7],"has":[8],"been":[9],"growing":[10],"research":[11],"interest":[12],"in":[13,107,202,207,219],"spiking":[14],"neural":[15,39],"networks":[16,40],"(SNNs)":[17],"due":[18],"to":[19,71,77,102,134,182],"their":[20,25],"energy":[21],"efficiency":[22],"resulting":[23],"from":[24],"multiplier-less":[26],"nature.":[27],"The":[28,56,73],"existing":[29],"methodologies":[30],"for":[31,213],"SNN":[32,139,208],"include":[34],"conversion":[36,70],"artificial":[38],"(ANNs)":[41],"into":[42,140,187],"equivalent":[43,188],"SNNs":[44,80,186,218],"or":[45],"emulation":[47],"ANNs,":[49],"with":[50,81],"two":[51],"crucial":[52],"challenges":[53,206],"yet":[54],"remaining.":[55],"first":[57,181],"challenge":[58,75],"involves":[59],"preserving":[60],"accuracy":[62,148,158],"original":[65],"ANN":[66],"models":[67],"during":[68],"SNNs.":[72],"second":[74],"is":[76,179],"run":[78],"complex":[79],"lower":[82],"latencies.":[83],"To":[84,171],"solve":[85],"problem":[87],"high":[89,93],"latency":[90],"while":[91,111,191],"maintaining":[92,192],"accuracy,":[94],"we":[95,125],"proposed":[96],"a":[97,108,113,128,154,167],"parallel":[98],"spike-generation":[99],"(PSG)":[100],"method":[101],"generate":[103],"all":[104],"spikes":[106],"single":[109,168],"timestep,":[110],"achieving":[112],"better":[114],"model":[115,152],"performance":[116],"than":[117],"standard":[119],"Integrate-and-Fire":[120],"model.":[121],"Based":[122],"on":[123,161],"PSG,":[124],"propose":[126],"OneSpike,":[127],"highly":[129],"effective":[130],"framework":[131],"that":[132,142],"helps":[133],"convert":[135],"any":[136],"rate-encoded":[137],"convolutional":[138],"one":[141,145],"uses":[143],"only":[144],"timestep":[146],"without":[147],"loss.":[149],"Our":[150],"OneSpike":[151],"achieves":[153],"state-of-the-art":[155],"(for":[156],"SNN)":[157],"81.92%":[160],"ImageNet":[163],"dataset":[164],"using":[165],"just":[166],"time":[169],"step.":[170],"best":[173],"our":[175,200],"knowledge,":[176],"this":[177],"study":[178],"explore":[183],"converting":[184],"multi-timestep":[185],"single-timestep":[189],"ones,":[190],"accuracy.":[193],"These":[194],"results":[195],"highlight":[196],"potential":[198],"approach":[201],"addressing":[203],"key":[205],"research,":[209],"paving":[210],"way":[212],"more":[214],"efficient":[215],"and":[216],"accurate":[217],"practical":[220],"applications.<sup":[221],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[222],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[223]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
