{"id":"https://openalex.org/W3115618622","doi":"https://doi.org/10.1109/apccas50809.2020.9301703","title":"A lightweight Max-Pooling method and architecture for Deep Spiking Convolutional Neural Networks","display_name":"A lightweight Max-Pooling method and architecture for Deep Spiking Convolutional Neural Networks","publication_year":2020,"publication_date":"2020-12-08","ids":{"openalex":"https://openalex.org/W3115618622","doi":"https://doi.org/10.1109/apccas50809.2020.9301703","mag":"3115618622"},"language":"en","primary_location":{"id":"doi:10.1109/apccas50809.2020.9301703","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apccas50809.2020.9301703","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://opus.lib.uts.edu.au/bitstream/10453/147605/3/1482986-ManualAPI%20am.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051880941","display_name":"Duy-Anh Nguyen","orcid":null},"institutions":[{"id":"https://openalex.org/I177233841","display_name":"Vietnam National University, Hanoi","ror":"https://ror.org/02jmfj006","country_code":"VN","type":"education","lineage":["https://openalex.org/I177233841"]}],"countries":["VN"],"is_corresponding":true,"raw_author_name":"Duy-Anh Nguyen","raw_affiliation_strings":["UTS-VNU Joint Technology and Innovation Research Centre (JTIRC)","University of Engineering and Technology, Vietnam National University, Hanoi"],"affiliations":[{"raw_affiliation_string":"UTS-VNU Joint Technology and Innovation Research Centre (JTIRC)","institution_ids":[]},{"raw_affiliation_string":"University of Engineering and Technology, Vietnam National University, Hanoi","institution_ids":["https://openalex.org/I177233841"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022587617","display_name":"Xuan\u2010Tu Tran","orcid":"https://orcid.org/0000-0003-4259-9579"},"institutions":[{"id":"https://openalex.org/I177233841","display_name":"Vietnam National University, Hanoi","ror":"https://ror.org/02jmfj006","country_code":"VN","type":"education","lineage":["https://openalex.org/I177233841"]},{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU","VN"],"is_corresponding":false,"raw_author_name":"Xuan-Tu Tran","raw_affiliation_strings":["University of Engineering and Technology, Vietnam National University, Hanoi","University of Technology Sydney"],"affiliations":[{"raw_affiliation_string":"University of Engineering and Technology, Vietnam National University, Hanoi","institution_ids":["https://openalex.org/I177233841"]},{"raw_affiliation_string":"University of Technology Sydney","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055889598","display_name":"Khanh N. Dang","orcid":"https://orcid.org/0000-0001-6702-3870"},"institutions":[{"id":"https://openalex.org/I177233841","display_name":"Vietnam National University, Hanoi","ror":"https://ror.org/02jmfj006","country_code":"VN","type":"education","lineage":["https://openalex.org/I177233841"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Khanh N. Dang","raw_affiliation_strings":["University of Engineering and Technology, Vietnam National University, Hanoi"],"affiliations":[{"raw_affiliation_string":"University of Engineering and Technology, Vietnam National University, Hanoi","institution_ids":["https://openalex.org/I177233841"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085512740","display_name":"Francesca Iacopi","orcid":"https://orcid.org/0000-0002-3196-0990"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Francesca Iacopi","raw_affiliation_strings":["University of Technology Sydney"],"affiliations":[{"raw_affiliation_string":"University of Technology Sydney","institution_ids":["https://openalex.org/I114017466"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5051880941"],"corresponding_institution_ids":["https://openalex.org/I177233841"],"apc_list":null,"apc_paid":null,"fwci":0.2079,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.53643623,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"209","last_page":"212"},"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":0.9980999827384949,"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/T12236","display_name":"Photoreceptor and optogenetics research","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.9529435634613037},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.750947117805481},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.6980330348014832},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6059762239456177},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5943349003791809},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5403987765312195},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5364096760749817},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.5219578146934509},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.520317554473877},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.495931476354599},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4955950975418091},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.46788400411605835},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.43089836835861206},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.39937806129455566},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3668490946292877},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34334540367126465},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10089430212974548},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.087553471326828}],"concepts":[{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.9529435634613037},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.750947117805481},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.6980330348014832},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6059762239456177},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5943349003791809},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5403987765312195},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5364096760749817},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.5219578146934509},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.520317554473877},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.495931476354599},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4955950975418091},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.46788400411605835},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.43089836835861206},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.39937806129455566},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3668490946292877},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34334540367126465},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10089430212974548},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.087553471326828},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/apccas50809.2020.9301703","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apccas50809.2020.9301703","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)","raw_type":"proceedings-article"},{"id":"pmh:oai:opus.lib.uts.edu.au:10453/147605","is_oa":true,"landing_page_url":"http://hdl.handle.net/10453/147605","pdf_url":"https://opus.lib.uts.edu.au/bitstream/10453/147605/3/1482986-ManualAPI%20am.pdf","source":{"id":"https://openalex.org/S4306401357","display_name":"UTS ePRESS (University of Technology Sydney)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I114017466","host_organization_name":"University of Technology Sydney","host_organization_lineage":["https://openalex.org/I114017466"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceeding"}],"best_oa_location":{"id":"pmh:oai:opus.lib.uts.edu.au:10453/147605","is_oa":true,"landing_page_url":"http://hdl.handle.net/10453/147605","pdf_url":"https://opus.lib.uts.edu.au/bitstream/10453/147605/3/1482986-ManualAPI%20am.pdf","source":{"id":"https://openalex.org/S4306401357","display_name":"UTS ePRESS (University of Technology Sydney)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I114017466","host_organization_name":"University of Technology Sydney","host_organization_lineage":["https://openalex.org/I114017466"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceeding"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3115618622.pdf","grobid_xml":"https://content.openalex.org/works/W3115618622.grobid-xml"},"referenced_works_count":5,"referenced_works":["https://openalex.org/W1645800954","https://openalex.org/W2775079417","https://openalex.org/W2783525259","https://openalex.org/W2947861270","https://openalex.org/W2964338223"],"related_works":["https://openalex.org/W4281699635","https://openalex.org/W4321472116","https://openalex.org/W3202619090","https://openalex.org/W3102040318","https://openalex.org/W4287724471","https://openalex.org/W2786930404","https://openalex.org/W3214713078","https://openalex.org/W3035000326","https://openalex.org/W2944910788","https://openalex.org/W3041589219"],"abstract_inverted_index":{"The":[0,19,59],"training":[1,47],"of":[2,17,21,35,52,82,106,136,146,180,186],"Deep":[3,24],"Spiking":[4],"Neural":[5,25],"Networks":[6,26],"(DSNNs)":[7],"is":[8,32,55,130,140,174],"facing":[9],"many":[10,42,101],"challenges":[11],"due":[12],"to":[13,28,113],"the":[14,36,50,65,79,104,134,143,147],"non-differentiable":[15],"nature":[16],"spikes.":[18,84],"conversion":[20,51,61],"a":[22,56,74,125,183],"traditional":[23],"(DNNs)":[27],"its":[29],"DSNNs":[30,99],"counterpart":[31],"currently":[33],"one":[34,96],"prominent":[37],"solutions,":[38],"as":[39,95],"it":[40],"leverages":[41],"state-of-the-art":[43,60],"pre-trained":[44],"models":[45],"and":[46,91,103,162],"techniques.":[48],"However,":[49],"max-pooling":[53,66,75,127],"layer":[54,67,111],"non-trivia":[57],"task.":[58],"methods":[62],"either":[63],"replace":[64],"with":[68,176],"other":[69],"pooling":[70],"mechanisms":[71],"or":[72],"use":[73],"method":[76],"based":[77,132,141],"on":[78,133,142,160],"cumulative":[80],"number":[81,105,135],"output":[83,107,137],"This":[85],"incurs":[86],"both":[87],"memory":[88],"storage":[89],"overhead":[90],"increases":[92],"computational":[93],"complexity,":[94],"inference":[97],"in":[98],"requires":[100],"timesteps,":[102],"spikes":[108,138],"after":[109],"each":[110],"needs":[112],"be":[114],"accumulated.":[115],"In":[116],"this":[117],"paper":[118],"<sup":[119],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[120],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[121],",":[122],"we":[123],"propose":[124],"novel":[126],"mechanism":[128],"that":[129,153,169],"not":[131],"but":[139],"membrane":[144],"potential":[145],"spiking":[148],"neurons.":[149],"Simulation":[150],"results":[151,167],"show":[152,168],"our":[154,170],"approach":[155],"still":[156],"preserves":[157],"classification":[158],"accuracies":[159],"MNIST":[161],"CIFARIO":[163],"dataset.":[164],"Hardware":[165],"implementation":[166],"proposed":[171],"hardware":[172],"block":[173],"lightweight":[175],"an":[177],"area":[178],"cost":[179],"15.3kGEs,":[181],"at":[182],"maximum":[184],"frequency":[185],"300":[187],"MHz.":[188]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}