{"id":"https://openalex.org/W4221160099","doi":"https://doi.org/10.1109/icassp43922.2022.9747906","title":"Rate Coding Or Direct Coding: Which One Is Better For Accurate, Robust, And Energy-Efficient Spiking Neural Networks?","display_name":"Rate Coding Or Direct Coding: Which One Is Better For Accurate, Robust, And Energy-Efficient Spiking Neural Networks?","publication_year":2022,"publication_date":"2022-04-27","ids":{"openalex":"https://openalex.org/W4221160099","doi":"https://doi.org/10.1109/icassp43922.2022.9747906"},"language":"en","primary_location":{"id":"doi:10.1109/icassp43922.2022.9747906","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9747906","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5082196231","display_name":"Youngeun Kim","orcid":"https://orcid.org/0000-0002-3542-7720"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Youngeun Kim","raw_affiliation_strings":["Yale University,USA","Yale University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University,USA","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Yale University, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002165905","display_name":"Hyoungseob Park","orcid":null},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hyoungseob Park","raw_affiliation_strings":["Yale University,USA","Yale University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University,USA","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Yale University, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050796355","display_name":"Abhishek Moitra","orcid":"https://orcid.org/0000-0002-0534-5206"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhishek Moitra","raw_affiliation_strings":["Yale University,USA","Yale University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University,USA","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Yale University, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004629816","display_name":"Abhiroop Bhattacharjee","orcid":"https://orcid.org/0000-0002-7721-271X"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhiroop Bhattacharjee","raw_affiliation_strings":["Yale University,USA","Yale University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University,USA","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Yale University, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079036687","display_name":"Yeshwanth Venkatesha","orcid":null},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yeshwanth Venkatesha","raw_affiliation_strings":["Yale University,USA","Yale University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University,USA","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Yale University, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050310538","display_name":"Priyadarshini Panda","orcid":"https://orcid.org/0000-0002-4167-6782"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Priyadarshini Panda","raw_affiliation_strings":["Yale University,USA","Yale University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University,USA","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Yale University, USA","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I32971472"],"apc_list":null,"apc_paid":null,"fwci":30.5646,"has_fulltext":false,"cited_by_count":81,"citation_normalized_percentile":{"value":0.9993489,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"71","last_page":"75"},"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9987999796867371,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9970999956130981,"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/computer-science","display_name":"Computer science","score":0.7463934421539307},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.5657395124435425},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.552886962890625},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46737876534461975},{"id":"https://openalex.org/keywords/tunstall-coding","display_name":"Tunstall coding","score":0.426314115524292},{"id":"https://openalex.org/keywords/context-adaptive-binary-arithmetic-coding","display_name":"Context-adaptive binary arithmetic coding","score":0.41969138383865356},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.351127028465271},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32673364877700806},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14496386051177979},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.13482129573822021},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09615454077720642}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7463934421539307},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.5657395124435425},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.552886962890625},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46737876534461975},{"id":"https://openalex.org/C73231260","wikidata":"https://www.wikidata.org/wiki/Q7853376","display_name":"Tunstall coding","level":4,"score":0.426314115524292},{"id":"https://openalex.org/C175732694","wikidata":"https://www.wikidata.org/wiki/Q1128713","display_name":"Context-adaptive binary arithmetic coding","level":3,"score":0.41969138383865356},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.351127028465271},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32673364877700806},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14496386051177979},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.13482129573822021},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09615454077720642},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp43922.2022.9747906","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp43922.2022.9747906","pdf_url":null,"source":{"id":"https://openalex.org/S4363607702","display_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8999999761581421,"id":"https://metadata.un.org/sdg/7"}],"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":37,"referenced_works":["https://openalex.org/W1570411240","https://openalex.org/W1945616565","https://openalex.org/W2112796928","https://openalex.org/W2140362090","https://openalex.org/W2289252105","https://openalex.org/W2513853720","https://openalex.org/W2621826044","https://openalex.org/W2892077605","https://openalex.org/W2899771611","https://openalex.org/W2963335874","https://openalex.org/W2964296416","https://openalex.org/W2990793844","https://openalex.org/W3007283957","https://openalex.org/W3007867923","https://openalex.org/W3015205410","https://openalex.org/W3023721287","https://openalex.org/W3042725081","https://openalex.org/W3093729901","https://openalex.org/W3097285691","https://openalex.org/W3102040318","https://openalex.org/W3102750118","https://openalex.org/W3118608800","https://openalex.org/W3134784623","https://openalex.org/W3201424517","https://openalex.org/W3203196551","https://openalex.org/W4206609022","https://openalex.org/W4293846201","https://openalex.org/W6640425456","https://openalex.org/W6739868092","https://openalex.org/W6756040250","https://openalex.org/W6771742384","https://openalex.org/W6774680395","https://openalex.org/W6784993802","https://openalex.org/W6785277872","https://openalex.org/W6786237780","https://openalex.org/W6787972765","https://openalex.org/W6792915618"],"related_works":["https://openalex.org/W1579872668","https://openalex.org/W1548215291","https://openalex.org/W1499106570","https://openalex.org/W2007687771","https://openalex.org/W4312628177","https://openalex.org/W2213686625","https://openalex.org/W2169930617","https://openalex.org/W2140825120","https://openalex.org/W2110189477","https://openalex.org/W2141441222"],"abstract_inverted_index":{"Recent":[0],"Spiking":[1],"Neural":[2],"Networks":[3],"(SNNs)":[4],"works":[5],"focus":[6],"on":[7,50,113,127],"an":[8,21,193],"image":[9,22],"classification":[10],"task,":[11],"therefore":[12],"various":[13,100],"coding":[14,30,33,64,97,111,125,137,152,167,174],"techniques":[15,98,112],"have":[16],"been":[17],"proposed":[18],"to":[19,60,156,160],"convert":[20],"into":[23],"temporal":[24],"binary":[25],"spikes.":[26],"Among":[27],"them,":[28],"rate":[29,151],"and":[31,88,102],"direct":[32,136,173],"are":[34],"regarded":[35],"as":[36,45],"prospective":[37],"candidates":[38],"for":[39,143,179,197],"building":[40,198],"a":[41,67,75,128,144],"practical":[42],"SNN":[43],"system":[44],"they":[46],"show":[47,134],"state-of-the-art":[48],"performance":[49,94],"large-scale":[51],"datasets.":[52,103],"Despite":[53],"their":[54],"usage,":[55],"there":[56],"is":[57,192],"little":[58],"attention":[59],"comparing":[61],"these":[62],"two":[63,80,96,114,124,189],"schemes":[65,126],"in":[66],"fair":[68],"manner.":[69],"In":[70,149],"this":[71],"paper,":[72],"we":[73,91,105,119],"conduct":[74],"comprehensive":[76],"analysis":[77],"of":[78,95,109,123,147,188],"the":[79,93,107,110,121,161,180,186],"codings":[81],"from":[82],"three":[83],"perspectives:":[84],"accuracy,":[85],"adversarial":[86,115,157],"robustness,":[87],"energy-efficiency.":[89],"First,":[90],"compare":[92,120],"with":[99],"architectures":[101],"Then,":[104],"measure":[106],"robustness":[108,155],"attack":[116],"methods.":[117],"Finally,":[118],"energy-efficiency":[122,171],"digital":[129],"hardware":[130],"platform.":[131],"Our":[132,183],"results":[133],"that":[135],"can":[138],"achieve":[139],"better":[140,154],"accuracy":[141],"especially":[142],"small":[145],"number":[146],"timesteps.":[148],"contrast,":[150],"shows":[153],"attacks":[158],"owing":[159],"non-differentiable":[162],"spike":[163],"generation":[164],"process.":[165],"Rate":[166],"also":[168],"yields":[169],"higher":[170],"than":[172],"which":[175,191],"requires":[176],"multi-bit":[177],"precision":[178],"first":[181],"layer.":[182],"study":[184],"explores":[185],"characteristics":[187],"codings,":[190],"important":[194],"design":[195],"consideration":[196],"SNNs1.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":32},{"year":2024,"cited_by_count":21},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":4}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}