{"id":"https://openalex.org/W4416250259","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228298","title":"Enabling Efficient Processing of Spiking Neural Networks with On-Chip Learning on Commodity Neuromorphic Processors for Edge AI Systems","display_name":"Enabling Efficient Processing of Spiking Neural Networks with On-Chip Learning on Commodity Neuromorphic Processors for Edge AI Systems","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250259","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228298"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228298","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228298","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2504.00957","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030422145","display_name":"Rachmad Vidya Wicaksana Putra","orcid":"https://orcid.org/0000-0001-8597-4530"},"institutions":[{"id":"https://openalex.org/I120250893","display_name":"New York University Abu Dhabi","ror":"https://ror.org/00e5k0821","country_code":"AE","type":"education","lineage":["https://openalex.org/I120250893","https://openalex.org/I57206974"]}],"countries":["AE"],"is_corresponding":true,"raw_author_name":"Rachmad Vidya Wicaksana Putra","raw_affiliation_strings":["New York University (NYU) Abu Dhabi,eBrain Lab,Abu Dhabi,UAE"],"affiliations":[{"raw_affiliation_string":"New York University (NYU) Abu Dhabi,eBrain Lab,Abu Dhabi,UAE","institution_ids":["https://openalex.org/I120250893"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055278214","display_name":"Pasindu Wickramasinghe","orcid":null},"institutions":[{"id":"https://openalex.org/I120250893","display_name":"New York University Abu Dhabi","ror":"https://ror.org/00e5k0821","country_code":"AE","type":"education","lineage":["https://openalex.org/I120250893","https://openalex.org/I57206974"]}],"countries":["AE"],"is_corresponding":false,"raw_author_name":"Pasindu Wickramasinghe","raw_affiliation_strings":["New York University (NYU) Abu Dhabi,eBrain Lab,Abu Dhabi,UAE"],"affiliations":[{"raw_affiliation_string":"New York University (NYU) Abu Dhabi,eBrain Lab,Abu Dhabi,UAE","institution_ids":["https://openalex.org/I120250893"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005190949","display_name":"Muhammad Shafique","orcid":"https://orcid.org/0000-0002-2607-8135"},"institutions":[{"id":"https://openalex.org/I120250893","display_name":"New York University Abu Dhabi","ror":"https://ror.org/00e5k0821","country_code":"AE","type":"education","lineage":["https://openalex.org/I120250893","https://openalex.org/I57206974"]}],"countries":["AE"],"is_corresponding":false,"raw_author_name":"Muhammad Shafique","raw_affiliation_strings":["New York University (NYU) Abu Dhabi,eBrain Lab,Abu Dhabi,UAE"],"affiliations":[{"raw_affiliation_string":"New York University (NYU) Abu Dhabi,eBrain Lab,Abu Dhabi,UAE","institution_ids":["https://openalex.org/I120250893"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5030422145"],"corresponding_institution_ids":["https://openalex.org/I120250893"],"apc_list":null,"apc_paid":null,"fwci":1.3643,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.84627775,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"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":0.9641000032424927,"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":0.9641000032424927,"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":0.012199999764561653,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.006000000052154064,"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/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.9559999704360962},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.7796000242233276},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.59579998254776},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5590000152587891},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5033000111579895},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.49900001287460327},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.4634999930858612},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.4575999975204468},{"id":"https://openalex.org/keywords/edge-computing","display_name":"Edge computing","score":0.3889999985694885}],"concepts":[{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.9559999704360962},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7971000075340271},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.7796000242233276},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.59579998254776},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5590000152587891},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5264000296592712},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.5098000168800354},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5033000111579895},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.49900001287460327},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.4634999930858612},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.4575999975204468},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.3889999985694885},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3840999901294708},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3840999901294708},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.3792000114917755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37439998984336853},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.3628999888896942},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3506999909877777},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.33889999985694885},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3093999922275543},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.30390000343322754},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2996000051498413},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.29179999232292175},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.29170000553131104},{"id":"https://openalex.org/C2984118289","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Power consumption","level":3,"score":0.2806999981403351},{"id":"https://openalex.org/C157170001","wikidata":"https://www.wikidata.org/wiki/Q4781507","display_name":"Applications of artificial intelligence","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228298","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228298","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2504.00957","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.00957","pdf_url":"https://arxiv.org/pdf/2504.00957","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2504.00957","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.00957","pdf_url":"https://arxiv.org/pdf/2504.00957","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320330069","display_name":"Tamkeen","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2108598243","https://openalex.org/W2790167166","https://openalex.org/W3109080038","https://openalex.org/W3176299681","https://openalex.org/W3195062233","https://openalex.org/W3199910846","https://openalex.org/W3201496044","https://openalex.org/W3206196385","https://openalex.org/W3206549115","https://openalex.org/W3211983404","https://openalex.org/W3214579303","https://openalex.org/W4280607449","https://openalex.org/W4290964162","https://openalex.org/W4293025114","https://openalex.org/W4312734020","https://openalex.org/W4383501716","https://openalex.org/W4385484717","https://openalex.org/W4400811418","https://openalex.org/W4401596736","https://openalex.org/W4404915002","https://openalex.org/W4406208739","https://openalex.org/W4412444494"],"related_works":[],"abstract_inverted_index":{"The":[0,133],"rising":[1],"demand":[2],"for":[3,48,94,103,124,154,160,184,224],"energy-efficient":[4],"edge":[5,49,221],"AI":[6,23,50,222],"systems":[7,223],"(e.g.,":[8,81],"mobile":[9],"agents/robots)":[10],"has":[11,39],"increased":[12],"the":[13,74,108,121,138,142,203,212,215],"interest":[14],"in":[15,164,171,218],"neuromorphic":[16,32,66,79],"computing,":[17],"since":[18],"it":[19],"offers":[20],"ultra-low":[21],"power/energy":[22],"computation":[24],"through":[25],"spiking":[26],"neural":[27],"network":[28,95],"(SNN)":[29],"algorithms":[30],"on":[31,64,107],"processors.":[33,67],"However,":[34],"their":[35],"efficient":[36,61,104,115,220],"implementation":[37,106],"strategy":[38,102],"not":[40],"been":[41],"comprehensively":[42],"studied,":[43],"hence":[44],"limiting":[45],"SNN":[46,62,105],"deployments":[47],"systems.":[51],"Toward":[52],"this,":[53,70],"we":[54,71,98,112],"propose":[55],"a":[56,100],"design":[57],"methodology":[58,140,217],"to":[59,90,119,126,144],"enable":[60],"processing":[63,193],"commodity":[65],"To":[68],"do":[69],"first":[72],"study":[73],"key":[75],"characteristics":[76],"of":[77,148,177,192,199,214],"targeted":[78,109],"hardware":[80],"memory":[82],"and":[83,86,130,167,174,195,207],"compute":[84],"budgets),":[85],"leverage":[87],"this":[88],"information":[89],"perform":[91],"compatibility":[92],"analysis":[93],"selection.":[96],"Afterward,":[97],"employ":[99],"mapping":[101],"processor.":[110],"Furthermore,":[111],"incorporate":[113],"an":[114],"on-chip":[116,178],"learning":[117,179],"mechanism":[118],"update":[120],"systems\u2019":[122],"knowledge":[123],"adapting":[125],"new":[127],"input":[128],"classes":[129],"dynamic":[131],"environments.":[132],"experimental":[134],"results":[135,210],"show":[136,211],"that":[137],"proposed":[139,216],"leads":[141],"system":[143],"achieve":[145],"low":[146,175],"latency":[147,176],"inference":[149],"(i.e.,":[150,180],"less":[151,157,168,181,189,196],"than":[152,158,169,182,190,197],"50ms":[153],"image":[155],"classification,":[156],"200ms":[159],"real-time":[161],"object":[162],"detection":[163],"video":[165],"streaming,":[166],"1ms":[170],"keyword":[172,185],"recognition)":[173],"2ms":[183],"recognition),":[186],"while":[187],"incurring":[188],"250mW":[191],"power":[194],"15mJ":[198],"energy":[200],"consumption":[201],"across":[202],"respective":[204],"different":[205],"applications":[206],"scenarios.":[208],"These":[209],"potential":[213],"enabling":[219],"diverse":[225],"application":[226],"use-cases.":[227]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-04-15T08:11:43.952461","created_date":"2025-10-10T00:00:00"}