{"id":"https://openalex.org/W2554166257","doi":"https://doi.org/10.1109/ijcnn.2016.7727303","title":"On the energy benefits of spiking deep neural networks: A case study","display_name":"On the energy benefits of spiking deep neural networks: A case study","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2554166257","doi":"https://doi.org/10.1109/ijcnn.2016.7727303","mag":"2554166257"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2016.7727303","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727303","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","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/A5100690519","display_name":"Bing Han","orcid":"https://orcid.org/0000-0002-6526-4432"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Bing Han","raw_affiliation_strings":["School of Electrical & Computer Engineering, Purdue University, West Lafayette, IN, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical & Computer Engineering, Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032635465","display_name":"Abhronil Sengupta","orcid":"https://orcid.org/0000-0002-5545-4494"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhronil Sengupta","raw_affiliation_strings":["School of Electrical & Computer Engineering, Purdue University, West Lafayette, IN, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical & Computer Engineering, Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031161187","display_name":"Kaushik Roy","orcid":"https://orcid.org/0009-0002-3375-2877"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kaushik Roy","raw_affiliation_strings":["School of Electrical & Computer Engineering, Purdue University, West Lafayette, IN, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical & Computer Engineering, Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100690519"],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":2.0214,"has_fulltext":false,"cited_by_count":47,"citation_normalized_percentile":{"value":0.8805334,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"971","last_page":"976"},"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":0.9979000091552734,"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":0.9911999702453613,"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/spiking-neural-network","display_name":"Spiking neural network","score":0.8999150395393372},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.8690329194068909},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7894675731658936},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.707319974899292},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6841298937797546},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6655135154724121},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5214297771453857},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4554474353790283}],"concepts":[{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.8999150395393372},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.8690329194068909},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7894675731658936},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.707319974899292},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6841298937797546},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6655135154724121},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5214297771453857},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4554474353790283}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2016.7727303","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727303","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6899999976158142,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1570411240","https://openalex.org/W1604973310","https://openalex.org/W1645800954","https://openalex.org/W2020676607","https://openalex.org/W2060969833","https://openalex.org/W2061124896","https://openalex.org/W2095705004","https://openalex.org/W2112796928","https://openalex.org/W2121458485","https://openalex.org/W2130360162","https://openalex.org/W2131763976","https://openalex.org/W2132424367","https://openalex.org/W2156640153","https://openalex.org/W2163605009","https://openalex.org/W2184188583","https://openalex.org/W2187281534","https://openalex.org/W2963542991","https://openalex.org/W6629368666","https://openalex.org/W6674330103","https://openalex.org/W6684191040","https://openalex.org/W6686207219","https://openalex.org/W6686999899"],"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/W3214713078","https://openalex.org/W2786930404","https://openalex.org/W3035000326","https://openalex.org/W2944910788","https://openalex.org/W3041589219"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"neural":[2,53,65,113,151],"networks":[3],"have":[4,56,70,95],"achieved":[5],"success":[6],"in":[7,32,38,115,149,154],"a":[8,126,130,134],"large":[9],"number":[10],"of":[11,34,91,133,158],"visual":[12],"processing":[13],"tasks":[14],"and":[15,26,62,106,142],"are":[16],"currently":[17],"utilized":[18],"for":[19,129],"many":[20],"real-world":[21],"applications":[22],"like":[23],"image":[24],"search":[25],"speech":[27],"recognition":[28],"among":[29],"others.":[30],"However,":[31,93],"spite":[33],"achieving":[35],"high":[36],"accuracy":[37],"such":[39],"classification":[40],"problems,":[41],"they":[42],"involve":[43],"significant":[44],"computational":[45],"resources.":[46],"Over":[47],"the":[48,59,86,102,139,145,155],"past":[49],"few":[50],"years,":[51],"artificial":[52,80],"network":[54],"models":[55],"evolved":[57],"into":[58],"biologically":[60],"realistic":[61],"event-driven":[63],"spiking":[64,83,112,156],"networks.":[66],"Recent":[67],"research":[68],"efforts":[69],"been":[71,96],"directed":[72],"at":[73],"developing":[74],"mechanisms":[75],"to":[76,82,117],"convert":[77],"traditional":[78],"deep":[79,111,136],"nets":[81,84,114],"where":[85],"neurons":[87],"communicate":[88],"by":[89,110],"means":[90],"spikes.":[92],"there":[94],"limited":[97],"studies":[98],"providing":[99],"insights":[100],"on":[101,138],"specific":[103],"power,":[104],"area":[105],"energy":[107],"benefits":[108],"offered":[109],"comparison":[116],"their":[118],"non-spiking":[119],"counterparts.":[120],"In":[121],"this":[122],"paper,":[123],"we":[124],"perform":[125],"case":[127],"study":[128],"hardware":[131],"implementation":[132],"spiking/non-spiking":[135],"net":[137],"MNIST":[140],"dataset":[141],"clearly":[143],"outline":[144],"design":[146],"prospects":[147],"involved":[148],"implementing":[150],"computing":[152],"platforms":[153],"mode":[157],"operation.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
