{"id":"https://openalex.org/W4410852789","doi":"https://doi.org/10.1109/tnnls.2025.3567567","title":"Converting High-Performance and Low-Latency SNNs Through Explicit Modeling of Residual Error in ANNs","display_name":"Converting High-Performance and Low-Latency SNNs Through Explicit Modeling of Residual Error in ANNs","publication_year":2025,"publication_date":"2025-05-29","ids":{"openalex":"https://openalex.org/W4410852789","doi":"https://doi.org/10.1109/tnnls.2025.3567567","pmid":"https://pubmed.ncbi.nlm.nih.gov/40440135"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2025.3567567","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2025.3567567","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://research.birmingham.ac.uk/en/publications/aa54df60-8ae1-46f2-a896-f893b0902f3f","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026487423","display_name":"Zhipeng Huang","orcid":"https://orcid.org/0009-0002-9883-3627"},"institutions":[{"id":"https://openalex.org/I111753288","display_name":"Fujian Normal University","ror":"https://ror.org/020azk594","country_code":"CN","type":"education","lineage":["https://openalex.org/I111753288"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhipeng Huang","raw_affiliation_strings":["Digital Fujian Internet-of-Things Laboratory of Environmental Monitoring, College of Computer and Cyber Security, Fujian Normal University, Fuzhou, Fujian, China"],"raw_orcid":"https://orcid.org/0009-0002-9883-3627","affiliations":[{"raw_affiliation_string":"Digital Fujian Internet-of-Things Laboratory of Environmental Monitoring, College of Computer and Cyber Security, Fujian Normal University, Fuzhou, Fujian, China","institution_ids":["https://openalex.org/I111753288"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030879654","display_name":"Jianhao Ding","orcid":"https://orcid.org/0000-0001-5171-1898"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianhao Ding","raw_affiliation_strings":["School of Computer Science and the State Key Laboratory of Multimedia Information Processing, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5171-1898","affiliations":[{"raw_affiliation_string":"School of Computer Science and the State Key Laboratory of Multimedia Information Processing, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080209384","display_name":"Zhiyu Pan","orcid":"https://orcid.org/0000-0001-5584-6669"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyu Pan","raw_affiliation_strings":["Institute for Artificial Intelligence and the School of Computer Science, Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Artificial Intelligence and the School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100327745","display_name":"Haoran Li","orcid":"https://orcid.org/0000-0003-2559-9585"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoran Li","raw_affiliation_strings":["Shaanxi Key Laboratory of Intelligent Human-Computer Interaction and Wearable Technology, Guangzhou Institute of Technology, Xidian University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shaanxi Key Laboratory of Intelligent Human-Computer Interaction and Wearable Technology, Guangzhou Institute of Technology, Xidian University, Guangzhou, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ying Fang","orcid":"https://orcid.org/0000-0002-8073-0964"},"institutions":[{"id":"https://openalex.org/I111753288","display_name":"Fujian Normal University","ror":"https://ror.org/020azk594","country_code":"CN","type":"education","lineage":["https://openalex.org/I111753288"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Fang","raw_affiliation_strings":["Fujian Provincial Key Laboratory of Statistics and Artificial Intelligence, Fujian Normal University, Fuzhou, Fujian, China"],"raw_orcid":"https://orcid.org/0000-0002-8073-0964","affiliations":[{"raw_affiliation_string":"Fujian Provincial Key Laboratory of Statistics and Artificial Intelligence, Fujian Normal University, Fuzhou, Fujian, China","institution_ids":["https://openalex.org/I111753288"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048087489","display_name":"Zhaofei Yu","orcid":"https://orcid.org/0000-0002-6913-7553"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaofei Yu","raw_affiliation_strings":["Institute for Artificial Intelligence and the School of Computer Science, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6913-7553","affiliations":[{"raw_affiliation_string":"Institute for Artificial Intelligence and the School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001710920","display_name":"Jian K. Liu","orcid":"https://orcid.org/0000-0002-5391-7213"},"institutions":[{"id":"https://openalex.org/I79619799","display_name":"University of Birmingham","ror":"https://ror.org/03angcq70","country_code":"GB","type":"education","lineage":["https://openalex.org/I79619799"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jian K. Liu","raw_affiliation_strings":["School of Computer Science, University of Birmingham, Birmingham, U.K"],"raw_orcid":"https://orcid.org/0000-0002-5391-7213","affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Birmingham, Birmingham, U.K","institution_ids":["https://openalex.org/I79619799"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7655,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.70449111,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"36","issue":"9","first_page":"16788","last_page":"16802"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9840999841690063,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9840999841690063,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9678999781608582,"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/T10320","display_name":"Neural Networks and Applications","score":0.9670000076293945,"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/residual","display_name":"Residual","score":0.8033834099769592},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.5641517043113708},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5612159967422485},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.4310646653175354},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.37233805656433105},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3350561261177063},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2953472137451172},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.0897112488746643}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.8033834099769592},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.5641517043113708},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5612159967422485},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.4310646653175354},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.37233805656433105},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3350561261177063},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2953472137451172},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0897112488746643}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tnnls.2025.3567567","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2025.3567567","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:40440135","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40440135","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null},{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/aa54df60-8ae1-46f2-a896-f893b0902f3f","is_oa":true,"landing_page_url":"https://research.birmingham.ac.uk/en/publications/aa54df60-8ae1-46f2-a896-f893b0902f3f","pdf_url":null,"source":{"id":"https://openalex.org/S4306402634","display_name":"University of Birmingham Research Portal (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Huang, Z, Ding, J, Pan, Z, Li, H, Fang, Y, Yu, Z & Liu, J K 2025, 'Converting High-Performance and Low-Latency SNNs Through Explicit Modeling of Residual Error in ANNs', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15. https://doi.org/10.1109/TNNLS.2025.3567567","raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/aa54df60-8ae1-46f2-a896-f893b0902f3f","is_oa":true,"landing_page_url":"https://research.birmingham.ac.uk/en/publications/aa54df60-8ae1-46f2-a896-f893b0902f3f","pdf_url":null,"source":{"id":"https://openalex.org/S4306402634","display_name":"University of Birmingham Research Portal (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Huang, Z, Ding, J, Pan, Z, Li, H, Fang, Y, Yu, Z & Liu, J K 2025, 'Converting High-Performance and Low-Latency SNNs Through Explicit Modeling of Residual Error in ANNs', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15. https://doi.org/10.1109/TNNLS.2025.3567567","raw_type":"article"},"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.5}],"awards":[{"id":"https://openalex.org/G1222315707","display_name":null,"funder_award_id":"62176003","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3833898155","display_name":null,"funder_award_id":"62088102","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3988691968","display_name":"\u53d1\u6398\u65f6\u95f4\u5e8f\u5217\u4e4b\u95f4\u52a8\u6001\u53d8\u5316\u7684\u56e0\u679c\u5173\u7cfb","funder_award_id":"2022J01656","funder_id":"https://openalex.org/F4320321878","funder_display_name":"Natural Science Foundation of Fujian Province"},{"id":"https://openalex.org/G5782943403","display_name":null,"funder_award_id":"20230484362","funder_id":"https://openalex.org/F4320334978","funder_display_name":"Beijing Nova Program"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321878","display_name":"Natural Science Foundation of Fujian Province","ror":null},{"id":"https://openalex.org/F4320334978","display_name":"Beijing Nova Program","ror":"https://ror.org/034k14f91"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1570411240","https://openalex.org/W1645800954","https://openalex.org/W1955857676","https://openalex.org/W2006370340","https://openalex.org/W2020676607","https://openalex.org/W2138913040","https://openalex.org/W2620474507","https://openalex.org/W2621826044","https://openalex.org/W2775079417","https://openalex.org/W2779025322","https://openalex.org/W2783525259","https://openalex.org/W2808550672","https://openalex.org/W2892077605","https://openalex.org/W2907516714","https://openalex.org/W2949736877","https://openalex.org/W2963206832","https://openalex.org/W2964338223","https://openalex.org/W2966081953","https://openalex.org/W2998119008","https://openalex.org/W3035644810","https://openalex.org/W3091431973","https://openalex.org/W3102750118","https://openalex.org/W3124478039","https://openalex.org/W3124546450","https://openalex.org/W3165499928","https://openalex.org/W3186962187","https://openalex.org/W3188250490","https://openalex.org/W3213590062","https://openalex.org/W4221157818","https://openalex.org/W4226443758","https://openalex.org/W4238614602","https://openalex.org/W4285605441","https://openalex.org/W4286221450","https://openalex.org/W4312527777","https://openalex.org/W4382450050","https://openalex.org/W4386071675","https://openalex.org/W4389776595"],"related_works":["https://openalex.org/W2560215812","https://openalex.org/W2949601986","https://openalex.org/W2788972299","https://openalex.org/W2521347458","https://openalex.org/W2498789492","https://openalex.org/W2729981612","https://openalex.org/W4233449973","https://openalex.org/W2925692864","https://openalex.org/W2768526084","https://openalex.org/W151988857"],"abstract_inverted_index":{"Spiking":[0],"neural":[1,22],"networks":[2,23],"(SNNs)":[3],"have":[4],"garnered":[5],"interest":[6],"due":[7],"to":[8,30,84,223],"their":[9,134],"energy":[10],"efficiency":[11],"and":[12,50,136,183,196,202,220],"superior":[13],"effectiveness":[14],"on":[15,108,147,175,180],"neuromorphic":[16,139,226],"chips":[17],"compared":[18,92],"with":[19,46,93,138],"traditional":[20],"artificial":[21],"(ANNs).":[24],"One":[25],"of":[26,44,54,69,102,106,123,150,164,172],"the":[27,35,40,47,61,67,89,94,100,103,121,124,161,165,170,181,191,203],"mainstream":[28],"approaches":[29],"implementing":[31],"deep":[32],"SNNs":[33,91,107,199],"is":[34,158,221,238],"ANN-SNN":[36,193],"conversion,":[37],"which":[38],"integrates":[39],"efficient":[41],"training":[42],"strategy":[43],"ANNs":[45],"energy-saving":[48],"potential":[49],"fast":[51],"inference":[52],"capability":[53],"SNNs.":[55],"However,":[56,128],"under":[57,217],"extremely":[58],"low-latency":[59],"conditions,":[60],"existing":[62],"conversion":[63,113,125,194],"theory":[64],"suggests":[65],"that":[66,187],"problem":[68,117],"SNNs'":[70],"neurons":[71],"firing":[72],"more":[73],"or":[74],"fewer":[75],"spikes":[76],"within":[77],"each":[78],"layer,":[79],"i.e.,":[80],"residual":[81,151,173],"error,":[82],"leads":[83],"a":[85,143],"performance":[86,216],"gap":[87],"in":[88],"converted":[90],"original":[95],"ANNs.":[96],"This":[97],"severely":[98],"limits":[99],"possibility":[101],"practical":[104,225],"application":[105],"delay-sensitive":[109],"edge":[110],"devices.":[111],"Existing":[112],"methods":[114,130,195],"addressing":[115],"this":[116],"usually":[118],"involve":[119],"modifying":[120],"state":[122],"spiking":[126],"neurons.":[127],"these":[129],"do":[131],"not":[132],"consider":[133],"adaptability":[135],"compatibility":[137],"chips.":[140],"We":[141],"propose":[142],"new":[144,211],"approach":[145,189],"based":[146],"explicit":[148],"modeling":[149],"errors":[152],"as":[153],"additive":[154],"noise.":[155],"The":[156,232],"noise":[157],"incorporated":[159],"into":[160],"activation":[162],"function":[163],"source":[166],"ANN,":[167],"effectively":[168],"reducing":[169],"impact":[171],"error":[174],"SNN":[176,215],"performance.":[177],"Our":[178],"experiments":[179],"CIFAR10/100":[182],"Tiny-ImageNet":[184],"datasets":[185],"verify":[186],"our":[188,208,235],"exceeds":[190],"prevailing":[192],"directly":[197],"trained":[198],"concerning":[200],"accuracy":[201],"required":[204],"time":[205],"steps.":[206],"Overall,":[207],"method":[209],"provides":[210],"ideas":[212],"for":[213,229,234],"improving":[214],"ultralow-latency":[218],"conditions":[219],"expected":[222],"promote":[224],"hardware":[227],"applications":[228],"further":[230],"development.":[231],"code":[233],"NQ":[236],"framework":[237],"available":[239],"at":[240],"https://github.com/hzp2022/ANN2SNN_NQ.":[241]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-13T07:31:44.756512","created_date":"2025-10-10T00:00:00"}