{"id":"https://openalex.org/W3208909186","doi":"https://doi.org/10.1109/tip.2021.3122092","title":"A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection","display_name":"A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection","publication_year":2021,"publication_date":"2021-04-21","ids":{"openalex":"https://openalex.org/W3208909186","doi":"https://doi.org/10.1109/tip.2021.3122092","mag":"3208909186"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2104.10719","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2104.10719","pdf_url":"https://arxiv.org/pdf/2104.10719","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2104.10719","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084263822","display_name":"Biswadeep Chakraborty","orcid":"https://orcid.org/0000-0002-6984-6907"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chakraborty, Biswadeep","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032748918","display_name":"Xueyuan She","orcid":"https://orcid.org/0000-0002-7372-5366"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"She, Xueyuan","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332 USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332 USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009591041","display_name":"Saibal Mukhopadhyay","orcid":"https://orcid.org/0000-0002-8894-3390"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mukhopadhyay, Saibal","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332 USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332 USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":52,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.9987000226974487,"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":0.998199999332428,"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/computer-science","display_name":"Computer science","score":0.8125258684158325},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.7757395505905151},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.6430646777153015},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6419920325279236},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6061068773269653},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5177940130233765},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5021307468414307},{"id":"https://openalex.org/keywords/spike","display_name":"Spike (software development)","score":0.47592809796333313},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4667437672615051},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4566246271133423},{"id":"https://openalex.org/keywords/propagation-of-uncertainty","display_name":"Propagation of uncertainty","score":0.44181081652641296},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.43593037128448486},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4163597524166107},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.340045690536499},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.23737230896949768}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8125258684158325},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.7757395505905151},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.6430646777153015},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6419920325279236},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6061068773269653},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5177940130233765},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5021307468414307},{"id":"https://openalex.org/C2781390188","wikidata":"https://www.wikidata.org/wiki/Q25203449","display_name":"Spike (software development)","level":2,"score":0.47592809796333313},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4667437672615051},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4566246271133423},{"id":"https://openalex.org/C123614077","wikidata":"https://www.wikidata.org/wiki/Q1364905","display_name":"Propagation of uncertainty","level":2,"score":0.44181081652641296},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.43593037128448486},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4163597524166107},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.340045690536499},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.23737230896949768},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:arXiv.org:2104.10719","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2104.10719","pdf_url":"https://arxiv.org/pdf/2104.10719","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2104.10719","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2104.10719","pdf_url":"https://arxiv.org/pdf/2104.10719","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2507828194","display_name":null,"funder_award_id":"#HR0011-17-2-0045","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G6241330985","display_name":null,"funder_award_id":"W911NF-19-1-0447","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"}],"funders":[{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W101771737","https://openalex.org/W582134693","https://openalex.org/W1630666735","https://openalex.org/W1645800954","https://openalex.org/W1849277567","https://openalex.org/W1861492603","https://openalex.org/W1958328135","https://openalex.org/W1982940446","https://openalex.org/W1990961106","https://openalex.org/W1999085092","https://openalex.org/W2003400208","https://openalex.org/W2006370340","https://openalex.org/W2009196600","https://openalex.org/W2009782005","https://openalex.org/W2036983007","https://openalex.org/W2048649579","https://openalex.org/W2049511526","https://openalex.org/W2055473437","https://openalex.org/W2063810326","https://openalex.org/W2108598243","https://openalex.org/W2136655611","https://openalex.org/W2138913040","https://openalex.org/W2149933564","https://openalex.org/W2155541015","https://openalex.org/W2188965883","https://openalex.org/W2194775991","https://openalex.org/W2417747970","https://openalex.org/W2471288921","https://openalex.org/W2562377059","https://openalex.org/W2562486519","https://openalex.org/W2621826044","https://openalex.org/W2775079417","https://openalex.org/W2779025322","https://openalex.org/W2791461106","https://openalex.org/W2796347433","https://openalex.org/W2798878556","https://openalex.org/W2809090039","https://openalex.org/W2896097393","https://openalex.org/W2897802248","https://openalex.org/W2910207440","https://openalex.org/W2946133197","https://openalex.org/W2963351448","https://openalex.org/W2963611739","https://openalex.org/W2963743287","https://openalex.org/W2964031418","https://openalex.org/W2964059111","https://openalex.org/W2964338223","https://openalex.org/W2968924835","https://openalex.org/W2998119008","https://openalex.org/W3005320883","https://openalex.org/W3005679856","https://openalex.org/W3007283957","https://openalex.org/W3023721287","https://openalex.org/W3033314398","https://openalex.org/W3038819247","https://openalex.org/W3048434603","https://openalex.org/W3092390915","https://openalex.org/W3102590949","https://openalex.org/W3104810215","https://openalex.org/W3106250896","https://openalex.org/W3128865642","https://openalex.org/W3166492204","https://openalex.org/W4293584584","https://openalex.org/W4294375521","https://openalex.org/W4294590608","https://openalex.org/W4299518610","https://openalex.org/W4367295640"],"related_works":["https://openalex.org/W3082178636","https://openalex.org/W2542565870","https://openalex.org/W4306175885","https://openalex.org/W4312604567","https://openalex.org/W4297619707","https://openalex.org/W4387390134","https://openalex.org/W4251092571","https://openalex.org/W2165312143","https://openalex.org/W2583316550","https://openalex.org/W3110622310"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,91],"Fully":[4],"Spiking":[5],"Hybrid":[6],"Neural":[7],"Network":[8],"(FSHNN)":[9],"for":[10],"energy-efficient":[11],"and":[12,45,85],"robust":[13],"object":[14,67,77],"detection":[15],"in":[16],"resource-constrained":[17],"platforms.":[18],"The":[19,31],"network":[20],"architecture":[21],"is":[22],"based":[23,66],"on":[24],"Convolutional":[25],"SNN":[26],"using":[27],"leaky-integrate-fire":[28],"neuron":[29],"models.":[30],"model":[32],"combines":[33],"unsupervised":[34],"Spike":[35],"Time-Dependent":[36],"Plasticity":[37],"(STDP)":[38],"learning":[39,43],"with":[40,90],"back-propagation":[41],"(STBP)":[42],"methods":[44],"also":[46,74],"uses":[47],"Monte":[48],"Carlo":[49],"Dropout":[50],"to":[51,64,81],"get":[52],"an":[53],"estimate":[54],"of":[55],"the":[56],"uncertainty":[57,93],"error.":[58,94],"FSHNN":[59],"provides":[60],"better":[61],"accuracy":[62],"compared":[63],"DNN":[65],"detectors":[68],"while":[69],"being":[70],"150X":[71],"energy-efficient.":[72],"It":[73],"outperforms":[75],"these":[76],"detectors,":[78],"when":[79],"subjected":[80],"noisy":[82],"input":[83],"data":[84,89],"less":[86],"labeled":[87],"training":[88],"lower":[92]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}
