{"id":"https://openalex.org/W2735289987","doi":"https://doi.org/10.1109/ijcnn.2017.7966154","title":"INXS: Bridging the throughput and energy gap for spiking neural networks","display_name":"INXS: Bridging the throughput and energy gap for spiking neural networks","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2735289987","doi":"https://doi.org/10.1109/ijcnn.2017.7966154","mag":"2735289987"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2017.7966154","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966154","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 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/A5103714575","display_name":"Surya Narayanan","orcid":null},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Surya Narayanan","raw_affiliation_strings":["School of Computing, University of Utah"],"affiliations":[{"raw_affiliation_string":"School of Computing, University of Utah","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067008084","display_name":"Ali Shafiee","orcid":"https://orcid.org/0000-0001-7154-9138"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ali Shafiee","raw_affiliation_strings":["School of Computing, University of Utah"],"affiliations":[{"raw_affiliation_string":"School of Computing, University of Utah","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087056095","display_name":"Rajeev Balasubramonian","orcid":"https://orcid.org/0009-0009-4093-5904"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rajeev Balasubramonian","raw_affiliation_strings":["School of Computing, University of Utah"],"affiliations":[{"raw_affiliation_string":"School of Computing, University of Utah","institution_ids":["https://openalex.org/I223532165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103714575"],"corresponding_institution_ids":["https://openalex.org/I223532165"],"apc_list":null,"apc_paid":null,"fwci":1.4334,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.83022597,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2451","last_page":"2459"},"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":0.9987999796867371,"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":0.8297462463378906},{"id":"https://openalex.org/keywords/memristor","display_name":"Memristor","score":0.7835564613342285},{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.7812694907188416},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.7033315896987915},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.666420042514801},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.6372684836387634},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.594802975654602},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5473140478134155},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.5075670480728149},{"id":"https://openalex.org/keywords/ibm","display_name":"IBM","score":0.5060781836509705},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.47964149713516235},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.46200060844421387},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.4223715662956238},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3917701542377472},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3537929058074951},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.09738266468048096},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.09168347716331482},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08496177196502686}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8297462463378906},{"id":"https://openalex.org/C150072547","wikidata":"https://www.wikidata.org/wiki/Q212923","display_name":"Memristor","level":2,"score":0.7835564613342285},{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.7812694907188416},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.7033315896987915},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.666420042514801},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.6372684836387634},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.594802975654602},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5473140478134155},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.5075670480728149},{"id":"https://openalex.org/C70388272","wikidata":"https://www.wikidata.org/wiki/Q5968558","display_name":"IBM","level":2,"score":0.5060781836509705},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.47964149713516235},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.46200060844421387},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.4223715662956238},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3917701542377472},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3537929058074951},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.09738266468048096},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.09168347716331482},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08496177196502686},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C171250308","wikidata":"https://www.wikidata.org/wiki/Q11468","display_name":"Nanotechnology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2017.7966154","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966154","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1604973310","https://openalex.org/W1645800954","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1964471912","https://openalex.org/W1967491101","https://openalex.org/W1981509416","https://openalex.org/W1985940938","https://openalex.org/W1995341919","https://openalex.org/W2005875039","https://openalex.org/W2009839417","https://openalex.org/W2020676607","https://openalex.org/W2048266589","https://openalex.org/W2067323599","https://openalex.org/W2082690044","https://openalex.org/W2083640863","https://openalex.org/W2097446068","https://openalex.org/W2114967216","https://openalex.org/W2121458485","https://openalex.org/W2130360162","https://openalex.org/W2131763976","https://openalex.org/W2133319764","https://openalex.org/W2138913040","https://openalex.org/W2141125852","https://openalex.org/W2152839228","https://openalex.org/W2155589054","https://openalex.org/W2163605009","https://openalex.org/W2166151045","https://openalex.org/W2183631084","https://openalex.org/W2233731247","https://openalex.org/W2237922334","https://openalex.org/W2285660444","https://openalex.org/W2314470091","https://openalex.org/W2442974303","https://openalex.org/W2508602506","https://openalex.org/W2518281301","https://openalex.org/W2549653513","https://openalex.org/W2550740543","https://openalex.org/W2556583623","https://openalex.org/W2906043559","https://openalex.org/W2950656546","https://openalex.org/W4236302577","https://openalex.org/W4243519499","https://openalex.org/W4251155475","https://openalex.org/W4254672563","https://openalex.org/W4403724184","https://openalex.org/W6684191040","https://openalex.org/W6686394896","https://openalex.org/W6730044877","https://openalex.org/W6739024043","https://openalex.org/W6764273044","https://openalex.org/W6825195337","https://openalex.org/W6836480157"],"related_works":["https://openalex.org/W1872623660","https://openalex.org/W4292697011","https://openalex.org/W3207218810","https://openalex.org/W3212508523","https://openalex.org/W1995352804","https://openalex.org/W2086672837","https://openalex.org/W2909534142","https://openalex.org/W4367187682","https://openalex.org/W3215957123","https://openalex.org/W1940420793"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"multiple":[3],"neuromorphic":[4],"architectures":[5,21,31],"have":[6,22],"been":[7],"designed":[8],"to":[9,56,144,161],"execute":[10,78],"cognitive":[11],"applications":[12],"that":[13,76,121,167],"deal":[14],"with":[15,37,49],"image":[16],"and":[17,62,87,127,136,179],"speech":[18],"analysis.":[19],"These":[20],"followed":[23],"one":[24],"of":[25,30,68,94,130,148],"two":[26],"approaches.":[27],"One":[28],"class":[29,43,71],"is":[32,44,72,173],"based":[33],"on":[34,46,82,91,151],"machine":[35],"learning":[36],"artificial":[38],"neural":[39,119],"networks.":[40],"A":[41,65],"second":[42,70],"focused":[45],"emulating":[47],"biology":[48],"spiking":[50,80,118],"neuron":[51,163],"models,":[52],"in":[53,100,107,154,175],"an":[54],"attempt":[55],"eventually":[57],"approach":[58],"the":[59,69,108,124,131,146,168],"brain's":[60],"accuracy":[61,90],"energy":[63,128,180],"efficiency.":[64,181],"prominent":[66],"example":[67],"IBM's":[73],"TrueNorth":[74,109,132],"processor":[75],"can":[77],"large":[79],"networks":[81,120],"a":[83,92,113],"low-power":[84],"tiled":[85],"architecture,":[86,115],"achieve":[88],"high":[89,177],"variety":[93],"tasks.":[95],"However,":[96],"as":[97],"we":[98],"show":[99,166],"this":[101],"work,":[102],"there":[103],"are":[104,158],"many":[105],"inefficiencies":[106],"design.":[110],"We":[111,165],"propose":[112],"new":[114],"INXS,":[116],"for":[117],"improves":[122],"upon":[123],"computational":[125],"efficiency":[126,129],"design":[133],"by":[134,171],"3,129\u00d7":[135],"10\u00d7":[137],"respectively.":[138],"The":[139],"architecture":[140],"uses":[141],"memristor":[142],"crossbars":[143,172],"compute":[145],"effects":[147],"input":[149],"spikes":[150],"several":[152],"neurons":[153],"parallel.":[155],"Digital":[156],"units":[157],"then":[159],"used":[160],"update":[162],"state.":[164],"parallelism":[169],"offered":[170],"critical":[174],"achieving":[176],"throughput":[178]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}