{"id":"https://openalex.org/W7163635807","doi":"https://doi.org/10.1109/access.2026.3700832","title":"Adaptive LIF-Based Output Neuron Design for Improved Accuracy in Integer-Only Digital Spiking Neural Networks","display_name":"Adaptive LIF-Based Output Neuron Design for Improved Accuracy in Integer-Only Digital Spiking Neural Networks","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7163635807","doi":"https://doi.org/10.1109/access.2026.3700832"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3700832","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3700832","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3700832","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035905395","display_name":"M J Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I94588446","display_name":"Hongik University","ror":"https://ror.org/00egdv862","country_code":"KR","type":"education","lineage":["https://openalex.org/I94588446"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Minkyung Kim","raw_affiliation_strings":["School of Electronics and Electrical Engineering, Hongik University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0009-0001-7924-3181","affiliations":[{"raw_affiliation_string":"School of Electronics and Electrical Engineering, Hongik University, Seoul, South Korea","institution_ids":["https://openalex.org/I94588446"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137955099","display_name":"Jiho Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I94588446","display_name":"Hongik University","ror":"https://ror.org/00egdv862","country_code":"KR","type":"education","lineage":["https://openalex.org/I94588446"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jiho Kim","raw_affiliation_strings":["School of Electronics and Electrical Engineering, Hongik University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0009-0003-2182-8181","affiliations":[{"raw_affiliation_string":"School of Electronics and Electrical Engineering, Hongik University, Seoul, South Korea","institution_ids":["https://openalex.org/I94588446"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101968025","display_name":"Yong Wook Kim","orcid":"https://orcid.org/0000-0002-5234-2454"},"institutions":[{"id":"https://openalex.org/I94588446","display_name":"Hongik University","ror":"https://ror.org/00egdv862","country_code":"KR","type":"education","lineage":["https://openalex.org/I94588446"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Youngmin Kim","raw_affiliation_strings":["School of Electronics and Electrical Engineering, Hongik University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-4524-2858","affiliations":[{"raw_affiliation_string":"School of Electronics and Electrical Engineering, Hongik University, Seoul, South Korea","institution_ids":["https://openalex.org/I94588446"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I94588446"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.75986919,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"91449","last_page":"91459"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.8093000054359436,"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":0.8093000054359436,"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":0.07490000128746033,"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":0.028999999165534973,"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/spiking-neural-network","display_name":"Spiking neural network","score":0.7556999921798706},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49720001220703125},{"id":"https://openalex.org/keywords/biological-neuron-model","display_name":"Biological neuron model","score":0.37540000677108765},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3411000072956085},{"id":"https://openalex.org/keywords/adaptive-filter","display_name":"Adaptive filter","score":0.3285999894142151}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8248999714851379},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.7556999921798706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.510699987411499},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49720001220703125},{"id":"https://openalex.org/C186565885","wikidata":"https://www.wikidata.org/wiki/Q1651163","display_name":"Biological neuron model","level":3,"score":0.37540000677108765},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3411000072956085},{"id":"https://openalex.org/C102248274","wikidata":"https://www.wikidata.org/wiki/Q168388","display_name":"Adaptive filter","level":2,"score":0.3285999894142151},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3052000105381012},{"id":"https://openalex.org/C2781390188","wikidata":"https://www.wikidata.org/wiki/Q25203449","display_name":"Spike (software development)","level":2,"score":0.29109999537467957},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.26350000500679016},{"id":"https://openalex.org/C33766855","wikidata":"https://www.wikidata.org/wiki/Q7189618","display_name":"Physical neural network","level":5,"score":0.257999986410141},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2529999911785126}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3700832","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3700832","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:96d68326e57f40ef8de49994fb97844e","is_oa":false,"landing_page_url":"https://doaj.org/article/96d68326e57f40ef8de49994fb97844e","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 91449-91459 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3700832","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3700832","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7980921864509583,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320338121","display_name":"Center of Innovation Program","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0,98],"work":[1],"presents":[2],"a":[3,22,77,93,106],"fully":[4],"digital,":[5],"fixed-weight":[6],"spiking":[7],"neural":[8],"network":[9],"(SNN)":[10],"architecture":[11,71,149],"for":[12],"deterministic":[13,124],"image":[14],"classification.":[15],"The":[16,148],"system":[17],"achieves":[18],"high-precision":[19],"classification":[20,134],"through":[21],"systematic":[23],"four-stage":[24],"optimization":[25],"process:":[26],"multi-feature":[27],"temporal":[28,48,173],"decoding,":[29],"adaptive":[30,45],"threshold":[31],"tuning,":[32],"neuron-specific":[33],"scaling,":[34],"and":[35,44,55,66,76,92,102,184],"ensemble-based":[36],"voting.":[37],"Output":[38],"neurons":[39],"integrate":[40],"leaky":[41],"membrane":[42],"potentials":[43],"thresholds,":[46],"while":[47],"evidence":[49],"is":[50],"encoded":[51],"using":[52,88,116],"spike":[53],"counts":[54],"first-spike":[56],"timing":[57],"via":[58],"ROM-stored":[59],"integer":[60,118],"coefficients.":[61],"To":[62],"improve":[63],"hardware":[64,84],"efficiency":[65,186],"enable":[67],"high-speed":[68],"inference,":[69],"the":[70,130,141,158],"incorporates":[72],"data-dependent":[73],"multiplication":[74],"skipping":[75],"resource-shared":[78],"spatial":[79],"accumulation":[80],"mechanism.":[81],"Furthermore,":[82],"critical":[83],"bottlenecks":[85],"are":[86,114],"resolved":[87],"2D":[89],"array":[90],"unrolling":[91],"two-stage":[94],"pipelined":[95],"decision":[96],"tree.":[97],"eliminates":[99],"routing":[100],"congestion":[101],"combinational":[103],"delays,":[104],"achieving":[105,154],"stable":[107],"100":[108],"MHz":[109],"operating":[110],"frequency.":[111],"All":[112],"computations":[113],"performed":[115],"fixed":[117],"arithmetic":[119],"without":[120,164],"additional":[121],"training,":[122],"ensuring":[123],"behavior.":[125],"RTL-level":[126],"evaluation":[127],"demonstrates":[128],"that":[129,170],"proposed":[131],"design":[132],"improves":[133],"accuracy":[135,156],"from":[136],"74.5%":[137],"to":[138],"98.7%":[139],"on":[140,157],"full":[142],"MNIST":[143],"test":[144],"set":[145],"(10,000":[146,162],"samples).":[147],"also":[150],"exhibits":[151],"strong":[152],"generalization,":[153],"87.4%":[155],"complex":[159],"Fashion-MNIST":[160],"dataset":[161],"samples)":[163],"parameter":[165],"retuning.":[166],"These":[167],"results":[168],"indicate":[169],"carefully":[171],"engineered":[172],"dynamics":[174],"combined":[175],"with":[176],"high-speed,":[177],"integer-only":[178],"processing":[179],"substantially":[180],"enhance":[181],"inference":[182],"throughput":[183],"energy":[185],"in":[187],"compact":[188],"digital":[189],"neuromorphic":[190],"processors.":[191]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-06-06T00:00:00"}