{"id":"https://openalex.org/W3132782973","doi":"https://doi.org/10.1109/vlsi-soc46417.2020.9344078","title":"Ultra-Compact, Scalable, Energy-Efficient $VO_{2}$ Insulator-Metal-Transition Oxide Based Spiking Neurons for Liquid State Machines","display_name":"Ultra-Compact, Scalable, Energy-Efficient $VO_{2}$ Insulator-Metal-Transition Oxide Based Spiking Neurons for Liquid State Machines","publication_year":2020,"publication_date":"2020-10-05","ids":{"openalex":"https://openalex.org/W3132782973","doi":"https://doi.org/10.1109/vlsi-soc46417.2020.9344078","mag":"3132782973"},"language":"en","primary_location":{"id":"doi:10.1109/vlsi-soc46417.2020.9344078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vlsi-soc46417.2020.9344078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IFIP/IEEE 28th International Conference on Very Large Scale Integration (VLSI-SOC)","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/A5003379315","display_name":"Samiran Ganguly","orcid":"https://orcid.org/0000-0002-6632-1574"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Samiran Ganguly","raw_affiliation_strings":["University of Virginia,Charles L. Brown Dept. of Electrical and Computer Engineering,Charlottesville,VA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Virginia,Charles L. Brown Dept. of Electrical and Computer Engineering,Charlottesville,VA,USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028883225","display_name":"Nikhil Shukla","orcid":"https://orcid.org/0000-0002-8899-5190"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nikhil Shukla","raw_affiliation_strings":["University of Virginia,Charles L. Brown Dept. of Electrical and Computer Engineering,Charlottesville,VA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Virginia,Charles L. Brown Dept. of Electrical and Computer Engineering,Charlottesville,VA,USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000227699","display_name":"Avik W. Ghosh","orcid":"https://orcid.org/0000-0002-8020-0760"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Avik W. Ghosh","raw_affiliation_strings":["University of Virginia,Charles L. Brown Dept. of Electrical and Computer Engineering,Charlottesville,VA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Virginia,Charles L. Brown Dept. of Electrical and Computer Engineering,Charlottesville,VA,USA","institution_ids":["https://openalex.org/I51556381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I51556381"],"apc_list":null,"apc_paid":null,"fwci":0.7157,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.68110972,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"345","issue":null,"first_page":"147","last_page":"152"},"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9986000061035156,"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"}},{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.9973000288009644,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.8813366889953613},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7142390608787537},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.6928520798683167},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6860673427581787},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6071962714195251},{"id":"https://openalex.org/keywords/cmos","display_name":"CMOS","score":0.5002455711364746},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4706548750400543},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.421680212020874},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.31543564796447754},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.21423611044883728},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.1589570939540863},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13957831263542175}],"concepts":[{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.8813366889953613},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7142390608787537},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.6928520798683167},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6860673427581787},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6071962714195251},{"id":"https://openalex.org/C46362747","wikidata":"https://www.wikidata.org/wiki/Q173431","display_name":"CMOS","level":2,"score":0.5002455711364746},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4706548750400543},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.421680212020874},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.31543564796447754},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.21423611044883728},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.1589570939540863},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13957831263542175},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vlsi-soc46417.2020.9344078","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vlsi-soc46417.2020.9344078","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IFIP/IEEE 28th International Conference on Very Large Scale Integration (VLSI-SOC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W12277440","https://openalex.org/W101771737","https://openalex.org/W1199176172","https://openalex.org/W1581808154","https://openalex.org/W1604973310","https://openalex.org/W1867196520","https://openalex.org/W1973315818","https://openalex.org/W1973613062","https://openalex.org/W1985940938","https://openalex.org/W1996157667","https://openalex.org/W2006370340","https://openalex.org/W2009375605","https://openalex.org/W2024529445","https://openalex.org/W2036971618","https://openalex.org/W2061191702","https://openalex.org/W2062593203","https://openalex.org/W2063695057","https://openalex.org/W2089051808","https://openalex.org/W2096581180","https://openalex.org/W2103179919","https://openalex.org/W2120475512","https://openalex.org/W2126176903","https://openalex.org/W2138913040","https://openalex.org/W2142875089","https://openalex.org/W2150354929","https://openalex.org/W2152435897","https://openalex.org/W2169074879","https://openalex.org/W2552750230","https://openalex.org/W2586468315","https://openalex.org/W2742216399","https://openalex.org/W2742240952","https://openalex.org/W2899002058","https://openalex.org/W3102481210","https://openalex.org/W3155801056","https://openalex.org/W4285719527","https://openalex.org/W6600493916","https://openalex.org/W6639133417","https://openalex.org/W6681937716","https://openalex.org/W6742248839","https://openalex.org/W6794361930"],"related_works":["https://openalex.org/W3089892344","https://openalex.org/W2960220682","https://openalex.org/W4313442939","https://openalex.org/W4386227293","https://openalex.org/W4372267706","https://openalex.org/W2885510266","https://openalex.org/W4288055417","https://openalex.org/W4205804651","https://openalex.org/W4287758233","https://openalex.org/W3136467750"],"abstract_inverted_index":{"Spiking":[0],"neural":[1,6,58],"networks,":[2],"inspired":[3],"by":[4,15],"biological":[5],"systems,":[7],"could":[8],"process":[9],"immense":[10],"volumes":[11],"of":[12,55],"spatio-temporal":[13],"data":[14],"representing":[16],"them":[17],"as":[18,60],"spikes.":[19],"Here,":[20],"we":[21,51],"propose":[22],"to":[23,43,79],"implement":[24],"compact,":[25],"scalable,":[26],"energy-efficient":[27],"spiking":[28],"neurons":[29],"based":[30,90],"on":[31],"the":[32,53],"unique":[33],"insulator-metal":[34],"transition":[35],"in":[36,70],"Vanadium":[37],"dioxide$(VO_{2})$which":[38],"interact":[39],"through":[40],"memristive":[41],"synapses,":[42],"emulate":[44],"a":[45,61,77],"Liquid":[46],"State":[47],"Machine":[48],"(LSM).":[49],"Further,":[50],"demonstrate":[52],"implementation":[54],"this":[56],"recurrent":[57],"network":[59],"temporal":[62],"auto-encoder,":[63],"and":[64],"adaptive":[65],"channel":[66],"equalizer":[67],"for":[68],"application":[69],"neuromorphic":[71],"signal":[72],"processing.":[73],"Our":[74],"approach":[75],"provides":[76],"pathway":[78],"reduce":[80],"component":[81],"count$(50\\":[82],"-100X)$and":[83],"improve":[84],"energy":[85],"efficiency$(>":[86],"50X)$over":[87],"conventional":[88],"CMOS":[89],"implementations.":[91]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}