{"id":"https://openalex.org/W2942898010","doi":"https://doi.org/10.1109/iscas.2019.8702132","title":"USCA: A Unified Systolic Convolution Array Architecture for Accelerating Sparse Neural Network","display_name":"USCA: A Unified Systolic Convolution Array Architecture for Accelerating Sparse Neural Network","publication_year":2019,"publication_date":"2019-05-01","ids":{"openalex":"https://openalex.org/W2942898010","doi":"https://doi.org/10.1109/iscas.2019.8702132","mag":"2942898010"},"language":"en","primary_location":{"id":"doi:10.1109/iscas.2019.8702132","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas.2019.8702132","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Symposium on Circuits and Systems (ISCAS)","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/A5101530396","display_name":"Wenjian Liu","orcid":"https://orcid.org/0000-0002-3136-3416"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenjian Liu","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019940239","display_name":"Jun Lin","orcid":"https://orcid.org/0000-0003-2760-4333"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Lin","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100696999","display_name":"Zhongfeng Wang","orcid":"https://orcid.org/0000-0002-7227-4786"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongfeng Wang","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I881766915"],"apc_list":null,"apc_paid":null,"fwci":0.6405,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.76734104,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9962000250816345,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.8410236239433289},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7465657591819763},{"id":"https://openalex.org/keywords/deconvolution","display_name":"Deconvolution","score":0.659644365310669},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5039789080619812},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.44702816009521484},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.4363175630569458},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.42350274324417114},{"id":"https://openalex.org/keywords/systolic-array","display_name":"Systolic array","score":0.4111831486225128},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.4054935872554779},{"id":"https://openalex.org/keywords/computational-science","display_name":"Computational science","score":0.3979560136795044},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3680904507637024},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.34504836797714233},{"id":"https://openalex.org/keywords/very-large-scale-integration","display_name":"Very-large-scale integration","score":0.31720787286758423},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2811526656150818},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26388055086135864},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.21248504519462585}],"concepts":[{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.8410236239433289},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7465657591819763},{"id":"https://openalex.org/C174576160","wikidata":"https://www.wikidata.org/wiki/Q1183700","display_name":"Deconvolution","level":2,"score":0.659644365310669},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5039789080619812},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.44702816009521484},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.4363175630569458},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.42350274324417114},{"id":"https://openalex.org/C150741067","wikidata":"https://www.wikidata.org/wiki/Q2377218","display_name":"Systolic array","level":3,"score":0.4111831486225128},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.4054935872554779},{"id":"https://openalex.org/C459310","wikidata":"https://www.wikidata.org/wiki/Q117801","display_name":"Computational science","level":1,"score":0.3979560136795044},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3680904507637024},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34504836797714233},{"id":"https://openalex.org/C14580979","wikidata":"https://www.wikidata.org/wiki/Q876049","display_name":"Very-large-scale integration","level":2,"score":0.31720787286758423},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2811526656150818},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26388055086135864},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.21248504519462585},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscas.2019.8702132","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas.2019.8702132","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8999999761581421}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2102605133","https://openalex.org/W2163605009","https://openalex.org/W2289252105","https://openalex.org/W2476548250","https://openalex.org/W2516141709","https://openalex.org/W2565851976","https://openalex.org/W2613577722","https://openalex.org/W2625457103","https://openalex.org/W2802238174","https://openalex.org/W2963674932","https://openalex.org/W2963840672","https://openalex.org/W4240168186","https://openalex.org/W6638632666","https://openalex.org/W6684191040","https://openalex.org/W6696085341","https://openalex.org/W6737901870"],"related_works":["https://openalex.org/W2369061952","https://openalex.org/W2367122702","https://openalex.org/W1601492201","https://openalex.org/W2132989621","https://openalex.org/W2015447694","https://openalex.org/W2383495548","https://openalex.org/W2370645350","https://openalex.org/W1969590113","https://openalex.org/W2038682752","https://openalex.org/W2592499194"],"abstract_inverted_index":{"Due":[0],"to":[1,15,34,39,81,161,182],"the":[2,90,101,116,134,175,183,190],"intensive":[3],"computational":[4],"complexity":[5],"and":[6,31,66,88,140,152,200],"various":[7,41],"types":[8,42,86],"of":[9,43,50,150,158,177,185],"convolution,":[10,52,58,198],"it":[11],"is":[12,112,121,172,189],"a":[13,21,77,105,124,147,156],"challange":[14],"implement":[16],"different":[17],"CNN":[18],"models":[19],"on":[20,28,63,115],"specific":[22],"hardware.":[23],"Many":[24],"previous":[25,59],"works":[26],"focus":[27],"data":[29],"reuse":[30],"sparsity":[32,91],"exploration":[33],"accelerate":[35],"computation":[36],"but":[37],"fail":[38],"support":[40,196],"convolution":[44,79,85,108,202],"efficiently.":[45],"When":[46],"dealing":[47],"with":[48,68,123],"variants":[49],"conventional":[51,197],"such":[53],"as":[54],"deconvolution":[55],"or":[56],"dilated":[57,201],"accelerators":[60],"waste":[61],"time":[62],"padding":[64,95],"zeroes":[65],"convolving":[67],"padded":[69],"feature":[70],"maps.":[71],"In":[72],"this":[73],"paper,":[74],"we":[75],"propose":[76],"unified":[78,106],"algorithm":[80],"intelligently":[82],"combine":[83],"several":[84],"together":[87],"exploit":[89],"in":[92,203],"activations.":[93],"The":[94,118,129],"process":[96],"can":[97,145,194],"be":[98],"skipped":[99],"by":[100,174],"proposed":[102],"algorithm.":[103,117],"Moreover,":[104],"systolic":[107],"array":[109],"(USCA)":[110],"architecture":[111,120,135,165,192],"developed":[113],"based":[114],"USCA":[119,164,188],"implemented":[122],"TSMC":[125],"28nm":[126],"CMOS":[127],"technology.":[128],"implementation":[130],"results":[131],"demonstrate":[132],"that":[133,193],"costs":[136],"206k":[137],"logic":[138],"gates":[139],"114.7kB":[141],"on-chip":[142],"memory.":[143],"It":[144],"reach":[146],"peak":[148],"performance":[149],"374.7GOPs":[151],"comsumes":[153],"201.1mW":[154],"at":[155],"frequency":[157],"1449MHz.":[159],"Compared":[160],"similar":[162],"works,":[163],"achieves":[166],"3":[167],"\u00d7":[168],"energy":[169],"efficiency,":[170],"which":[171],"measured":[173],"number":[176],"GOPS":[178],"per":[179],"watt.":[180],"Besides,":[181],"best":[184],"our":[186],"knowledge,":[187],"first":[191],"simultaneously":[195],"deconvolution,":[199],"an":[204],"efficient":[205],"way.":[206]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}