{"id":"https://openalex.org/W3214713078","doi":"https://doi.org/10.1109/dac18074.2021.9586323","title":"In-Hardware Learning of Multilayer Spiking Neural Networks on a Neuromorphic Processor","display_name":"In-Hardware Learning of Multilayer Spiking Neural Networks on a Neuromorphic Processor","publication_year":2021,"publication_date":"2021-11-08","ids":{"openalex":"https://openalex.org/W3214713078","doi":"https://doi.org/10.1109/dac18074.2021.9586323","mag":"3214713078"},"language":"en","primary_location":{"id":"doi:10.1109/dac18074.2021.9586323","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac18074.2021.9586323","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 58th ACM/IEEE Design Automation Conference (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/A5053617387","display_name":"Amar Shrestha","orcid":"https://orcid.org/0000-0002-5988-0466"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amar Shrestha","raw_affiliation_strings":["Syracuse University, Syracuse, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, Syracuse, USA","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112473990","display_name":"Haowen Fang","orcid":"https://orcid.org/0009-0009-7551-3373"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haowen Fang","raw_affiliation_strings":["Syracuse University, Syracuse, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, Syracuse, USA","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066394369","display_name":"Daniel Patrick Rider","orcid":"https://orcid.org/0000-0002-3644-7088"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Patrick Rider","raw_affiliation_strings":["Syracuse University, Syracuse, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, Syracuse, USA","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016010483","display_name":"Zaidao Mei","orcid":"https://orcid.org/0009-0007-0959-9696"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zaidao Mei","raw_affiliation_strings":["Syracuse University, Syracuse, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, Syracuse, USA","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018468480","display_name":"Qinru Qiu","orcid":"https://orcid.org/0000-0003-2546-0655"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qinru Qiu","raw_affiliation_strings":["Syracuse University, Syracuse, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, Syracuse, USA","institution_ids":["https://openalex.org/I70983195"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I70983195"],"apc_list":null,"apc_paid":null,"fwci":10.5821,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.99352993,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"367","last_page":"372"},"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/T10581","display_name":"Neural dynamics and brain function","score":0.9984999895095825,"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"}},{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9979000091552734,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.9740108251571655},{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.9408127069473267},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.8878001570701599},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7995002269744873},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.6610438823699951},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.6032940745353699},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5487091541290283},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4668353796005249},{"id":"https://openalex.org/keywords/hardware-acceleration","display_name":"Hardware acceleration","score":0.43090444803237915},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.340520977973938},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.33552899956703186},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3233792781829834},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.1375213861465454}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.9740108251571655},{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.9408127069473267},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.8878001570701599},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7995002269744873},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.6610438823699951},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.6032940745353699},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5487091541290283},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4668353796005249},{"id":"https://openalex.org/C13164978","wikidata":"https://www.wikidata.org/wiki/Q600158","display_name":"Hardware acceleration","level":3,"score":0.43090444803237915},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.340520977973938},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.33552899956703186},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3233792781829834},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.1375213861465454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dac18074.2021.9586323","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac18074.2021.9586323","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 58th ACM/IEEE Design Automation Conference (DAC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8899999856948853,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1645800954","https://openalex.org/W1855112655","https://openalex.org/W2183631084","https://openalex.org/W2283854151","https://openalex.org/W2308200533","https://openalex.org/W2507556850","https://openalex.org/W2552737632","https://openalex.org/W2565565355","https://openalex.org/W2594491944","https://openalex.org/W2621826044","https://openalex.org/W2750384547","https://openalex.org/W2783525259","https://openalex.org/W2898344924","https://openalex.org/W2962684665","https://openalex.org/W2963150511","https://openalex.org/W2963206832","https://openalex.org/W2964115671","https://openalex.org/W2965390876","https://openalex.org/W2971713705","https://openalex.org/W2972809290","https://openalex.org/W2982433817","https://openalex.org/W3034451759","https://openalex.org/W3118608800","https://openalex.org/W6686394896","https://openalex.org/W6698390431","https://openalex.org/W6752051825","https://openalex.org/W6752300446","https://openalex.org/W6755662464","https://openalex.org/W6768238304","https://openalex.org/W7027401133"],"related_works":["https://openalex.org/W3137378424","https://openalex.org/W2809732489","https://openalex.org/W4287780255","https://openalex.org/W3023361272","https://openalex.org/W4391092513","https://openalex.org/W3008580913","https://openalex.org/W2896628485","https://openalex.org/W4281699635","https://openalex.org/W1701967867","https://openalex.org/W4321472116"],"abstract_inverted_index":{"Although":[0],"widely":[1],"used":[2],"in":[3,48],"machine":[4],"learning,":[5],"backpropagation":[6,33],"cannot":[7],"directly":[8],"be":[9],"applied":[10],"to":[11,44],"SNN":[12],"training":[13],"and":[14,26,41,81,87,89],"is":[15,54],"not":[16],"feasible":[17],"on":[18,56,77],"a":[19,31,49,91],"neuromorphic":[20,50],"processor":[21],"that":[22],"emulates":[23],"biological":[24,36],"neuron":[25],"synapses.":[27],"This":[28],"work":[29],"presents":[30],"spike-based":[32],"algorithm":[34,53],"with":[35,84,97],"plausible":[37],"local":[38],"update":[39],"rules":[40],"adapts":[42],"it":[43],"fit":[45],"the":[46,98],"constraint":[47],"hardware.":[51],"The":[52],"implemented":[55],"Intel\u2019s":[57],"Loihi":[58],"chip":[59],"enabling":[60],"low":[61],"power":[62],"in-hardware":[63],"supervised":[64],"online":[65,95],"learning":[66,96],"of":[67,93],"multilayered":[68],"SNNs":[69],"for":[70],"mobile":[71],"applications.":[72],"We":[73],"test":[74],"this":[75],"implementation":[76],"MNIST,":[78],"Fashion-MNIST,":[79],"CIFAR-10":[80],"MSTAR":[82],"datasets":[83],"promising":[85],"performance":[86],"energy-efficiency,":[88],"demonstrate":[90],"possibility":[92],"incremental":[94],"implementation.":[99]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}