{"id":"https://openalex.org/W7169017926","doi":"https://doi.org/10.1145/3822454.3822457","title":"Quantization of Spiking Neural Networks Beyond Accuracy","display_name":"Quantization of Spiking Neural Networks Beyond Accuracy","publication_year":2026,"publication_date":"2026-07-16","ids":{"openalex":"https://openalex.org/W7169017926","doi":"https://doi.org/10.1145/3822454.3822457"},"language":null,"primary_location":{"id":"doi:10.1145/3822454.3822457","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3822454.3822457","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Neuromorphic Systems","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3822454.3822457","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5141003393","display_name":"Evan Smith","orcid":"https://orcid.org/0009-0004-3253-299X"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Evan Smith","raw_affiliation_strings":["Worcester Polytechnic Institute, Worcester, MA, USA"],"raw_orcid":"https://orcid.org/0009-0004-3253-299X","affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute, Worcester, MA, USA","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027788582","display_name":"Jacob Whitehill","orcid":"https://orcid.org/0000-0002-5851-312X"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jacob Whitehill","raw_affiliation_strings":["Worcester Polytechnic Institute, Worcester, MA, USA"],"raw_orcid":"https://orcid.org/0000-0002-5851-312X","affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute, Worcester, MA, USA","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017195195","display_name":"Fatemeh Ganji","orcid":"https://orcid.org/0000-0003-0151-1307"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fatemeh Ganji","raw_affiliation_strings":["Worcester Polytechnic Institute, Worcester, MA, USA"],"raw_orcid":"https://orcid.org/0000-0003-0151-1307","affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute, Worcester, MA, USA","institution_ids":["https://openalex.org/I107077323"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I107077323"],"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":null,"issue":null,"first_page":"226","last_page":"232"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.8198999762535095},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6310999989509583},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4602000117301941},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.3714999854564667},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.3614000082015991},{"id":"https://openalex.org/keywords/weight-distribution","display_name":"Weight distribution","score":0.3273000121116638}],"concepts":[{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.8198999762535095},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6310999989509583},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.589900016784668},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.48399999737739563},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4602000117301941},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4456999897956848},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.3714999854564667},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3614000082015991},{"id":"https://openalex.org/C25432639","wikidata":"https://www.wikidata.org/wiki/Q3711790","display_name":"Weight distribution","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.3100000023841858},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.29910001158714294},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2976999878883362},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27140000462532043},{"id":"https://openalex.org/C2776848632","wikidata":"https://www.wikidata.org/wiki/Q853463","display_name":"Clipping (morphology)","level":2,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3822454.3822457","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3822454.3822457","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Neuromorphic Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3822454.3822457","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3822454.3822457","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Neuromorphic Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1604973310","https://openalex.org/W2143668817","https://openalex.org/W2783525259","https://openalex.org/W2884150179","https://openalex.org/W2996263355","https://openalex.org/W4323045263","https://openalex.org/W4402244822","https://openalex.org/W4403791196"],"related_works":[],"abstract_inverted_index":{"Quantization":[0],"is":[1,131],"a":[2,37,96,165],"natural":[3],"complement":[4],"to":[5,68,73,142],"the":[6,41,143],"sparse,":[7],"event-driven":[8],"computation":[9],"of":[10,44],"Spiking":[11],"Neural":[12],"Networks,":[13],"reducing":[14],"memory":[15],"bandwidth":[16],"and":[17,55,83,103,109,118,161],"arithmetic":[18],"cost":[19],"for":[20,99,168],"deployment":[21],"on":[22,33,112,116],"resource-constrained":[23],"hardware.":[24],"However,":[25],"existing":[26],"SNN":[27],"quantization":[28,51,111,124,137],"evaluation":[29,157],"focuses":[30],"almost":[31],"exclusively":[32],"accuracy,":[34,65,160],"overlooking":[35],"whether":[36],"quantized":[38],"network":[39],"preserves":[40],"firing":[42,61,76,100,139],"behavior":[43,140,150],"its":[45],"full-precision":[46,144],"counterpart.":[47],"We":[48,120],"demonstrate":[49],"that":[50,122,149,162],"method,":[52],"clipping":[53],"range,":[54],"bit-width":[56],"can":[57],"produce":[58],"substantially":[59],"different":[60],"distributions":[62],"at":[63],"equivalent":[64],"differences":[66],"invisible":[67],"standard":[69],"metrics":[70],"but":[71],"relevant":[72],"deployment,":[74],"where":[75],"activity":[77],"governs":[78],"effective":[79],"sparsity,":[80],"state":[81],"storage,":[82],"event-processing":[84],"load.":[85],"To":[86],"capture":[87],"this":[88],"gap,":[89],"we":[90],"propose":[91],"Earth":[92],"Mover\u2019s":[93],"Distance":[94],"as":[95,155],"diagnostic":[97],"metric":[98],"distribution":[101],"divergence,":[102],"apply":[104],"it":[105],"systematically":[106],"across":[107],"weight":[108],"membrane":[110],"SEW-ResNet":[113],"architectures":[114],"trained":[115],"CIFAR-10":[117],"CIFAR-100.":[119],"find":[121],"uniform":[123],"induces":[125],"distributional":[126],"drift":[127],"even":[128],"when":[129],"accuracy":[130],"preserved,":[132],"while":[133],"LQ-Net":[134],"style":[135],"learned":[136],"maintains":[138],"close":[141],"baseline.":[145],"Our":[146],"results":[147],"suggest":[148],"preservation":[151],"should":[152],"be":[153],"treated":[154],"an":[156],"criterion":[158],"alongside":[159],"EMD":[163],"provides":[164],"principled":[166],"tool":[167],"assessing":[169],"it.":[170]},"counts_by_year":[],"updated_date":"2026-07-17T05:58:32.477365","created_date":"2026-07-17T00:00:00"}