{"id":"https://openalex.org/W3200747779","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534037","title":"Systolic-Array Spiking Neural Accelerators with Dynamic Heterogeneous Voltage Regulation","display_name":"Systolic-Array Spiking Neural Accelerators with Dynamic Heterogeneous Voltage Regulation","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3200747779","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534037","mag":"3200747779"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9534037","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534037","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","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/A5070891492","display_name":"Jeong-Jun Lee","orcid":"https://orcid.org/0000-0002-7370-889X"},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeong-Jun Lee","raw_affiliation_strings":["Electrical and Computer Engineering, University of California, Santa Barbara, CA, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, University of California, Santa Barbara, CA, United States","institution_ids":["https://openalex.org/I154570441"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101728575","display_name":"Jianhao Chen","orcid":"https://orcid.org/0000-0001-8939-9641"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianhao Chen","raw_affiliation_strings":["Electrical and Computer Engineering, Texas A&M University, College Station, TX, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, Texas A&M University, College Station, TX, United States","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100773215","display_name":"Wenrui Zhang","orcid":"https://orcid.org/0000-0003-1004-4499"},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenrui Zhang","raw_affiliation_strings":["Electrical and Computer Engineering, University of California, Santa Barbara, CA, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, University of California, Santa Barbara, CA, United States","institution_ids":["https://openalex.org/I154570441"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100432782","display_name":"Peng Li","orcid":"https://orcid.org/0000-0003-3548-4589"},"institutions":[{"id":"https://openalex.org/I154570441","display_name":"University of California, Santa Barbara","ror":"https://ror.org/02t274463","country_code":"US","type":"education","lineage":["https://openalex.org/I154570441"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peng Li","raw_affiliation_strings":["Electrical and Computer Engineering, University of California, Santa Barbara, CA, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, University of California, Santa Barbara, CA, United States","institution_ids":["https://openalex.org/I154570441"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8141,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.65425819,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"10","issue":null,"first_page":"1","last_page":"7"},"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":0.9990000128746033,"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":0.9983000159263611,"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/computer-science","display_name":"Computer science","score":0.7392090559005737},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.7124633193016052},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.5558428168296814},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5493079423904419},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5232549905776978},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5138750076293945},{"id":"https://openalex.org/keywords/systolic-array","display_name":"Systolic array","score":0.5135155320167542},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.490579754114151},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.44231632351875305},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.43445074558258057},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.3919847011566162},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2953985929489136},{"id":"https://openalex.org/keywords/very-large-scale-integration","display_name":"Very-large-scale integration","score":0.19469952583312988},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14668798446655273},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.1173962950706482},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.07736596465110779}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7392090559005737},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.7124633193016052},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.5558428168296814},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5493079423904419},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5232549905776978},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5138750076293945},{"id":"https://openalex.org/C150741067","wikidata":"https://www.wikidata.org/wiki/Q2377218","display_name":"Systolic array","level":3,"score":0.5135155320167542},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.490579754114151},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.44231632351875305},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.43445074558258057},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3919847011566162},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2953985929489136},{"id":"https://openalex.org/C14580979","wikidata":"https://www.wikidata.org/wiki/Q876049","display_name":"Very-large-scale integration","level":2,"score":0.19469952583312988},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14668798446655273},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.1173962950706482},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.07736596465110779},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9534037","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534037","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9100000262260437,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G6557383758","display_name":"Enabling Adaptive Voltage Regulation: Control, Machine Learning, and Circuit Design","funder_award_id":"2000851","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W345956458","https://openalex.org/W1604973310","https://openalex.org/W2044535169","https://openalex.org/W2067523571","https://openalex.org/W2069851236","https://openalex.org/W2085332361","https://openalex.org/W2109596721","https://openalex.org/W2114601852","https://openalex.org/W2116175063","https://openalex.org/W2117696986","https://openalex.org/W2150909864","https://openalex.org/W2152839228","https://openalex.org/W2289252105","https://openalex.org/W2513853720","https://openalex.org/W2520854767","https://openalex.org/W2612055717","https://openalex.org/W2745933219","https://openalex.org/W2783525259","https://openalex.org/W2900228909","https://openalex.org/W2911491685","https://openalex.org/W2912311811","https://openalex.org/W2952294252","https://openalex.org/W2953384591","https://openalex.org/W2961245123","https://openalex.org/W2962684665","https://openalex.org/W2963834742","https://openalex.org/W2970338293","https://openalex.org/W3022930763","https://openalex.org/W3028593627","https://openalex.org/W3041495229","https://openalex.org/W3114897136","https://openalex.org/W3116977674","https://openalex.org/W3146763006","https://openalex.org/W4238614602","https://openalex.org/W4287811529","https://openalex.org/W4298355310","https://openalex.org/W6677377052","https://openalex.org/W6713134421","https://openalex.org/W6726592036","https://openalex.org/W6749922295","https://openalex.org/W6752051825","https://openalex.org/W6758823024","https://openalex.org/W6765111094","https://openalex.org/W6766330063","https://openalex.org/W6777291753","https://openalex.org/W6787561339","https://openalex.org/W6787696269"],"related_works":["https://openalex.org/W2000785801","https://openalex.org/W986318368","https://openalex.org/W2384410913","https://openalex.org/W2352878646","https://openalex.org/W2004734601","https://openalex.org/W2130149817","https://openalex.org/W2990194547","https://openalex.org/W1480123525","https://openalex.org/W2620865396","https://openalex.org/W4319431385"],"abstract_inverted_index":{"Spiking":[0],"neural":[1,11,34,87,143,184],"networks":[2,144],"(SNNs)":[3],"have":[4],"emerged":[5],"as":[6],"a":[7,14,43,100,117,172,175],"new":[8],"generation":[9],"of":[10,32,112,127,137],"networks,":[12],"presenting":[13],"brain-inspired":[15],"event-driven":[16],"model":[17],"with":[18,147],"advantages":[19],"in":[20,68,178],"spatiotemporal":[21],"information":[22],"processing.":[23],"Due":[24],"to":[25,46,89,139,162,171],"the":[26,76,148,165],"need":[27],"for":[28,55,85,130,182],"high":[29,109],"power":[30,37,48,96,159],"consumption":[31],"compute-intensive":[33],"accelerators,":[35],"adequate":[36],"delivery":[38],"network":[39],"(PDN)":[40],"design":[41,54],"is":[42],"key":[44],"requirement":[45],"ensure":[47],"efficiency":[49,93,111],"and":[50,124],"integrity.":[51,97],"However,":[52],"PDN":[53,104,131,167],"SNN":[56],"accelerators":[57,88],"has":[58],"not":[59],"been":[60],"extensively":[61],"studied":[62],"despite":[63],"its":[64],"great":[65],"potential":[66],"benefit":[67],"energy":[69,92,110,180],"efficiency.":[70],"In":[71],"this":[72],"paper,":[73],"we":[74,133,156],"present":[75],"first":[77],"study":[78],"on":[79,116,186],"dynamic":[80,103,149],"heterogeneous":[81],"voltage":[82],"regulation":[83],"(HVR)":[84],"spiking":[86,114,128,141,183],"maximize":[90],"system":[91],"while":[94],"ensuring":[95],"We":[98],"propose":[99],"novel":[101],"sparse-workload-aware":[102],"control":[105],"policy,":[106],"which":[107,169],"enables":[108],"sparse":[113,122],"computation":[115],"systolic":[118,187],"array.":[119],"By":[120],"exploring":[121],"inputs":[123],"all-or-none":[125],"nature":[126],"computations":[129,185],"control,":[132],"explore":[134],"different":[135],"types":[136],"PDNs":[138],"accelerate":[140],"convolutional":[142],"(S-CNNs)":[145],"trained":[146],"vision":[150],"sensor":[151],"(DVS)":[152],"gesture":[153],"dataset.":[154],"Furthermore,":[155],"demonstrate":[157],"various":[158],"gating":[160],"schemes":[161],"further":[163],"optimize":[164],"proposed":[166],"architecture,":[168],"leads":[170],"more":[173],"than":[174],"three-fold":[176],"reduction":[177],"total":[179],"overhead":[181],"array-based":[188],"accelerators.":[189]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}
