{"id":"https://openalex.org/W3158956267","doi":"https://doi.org/10.1109/iscas51556.2021.9401287","title":"Accelerating Convolutional Neural Network Inference Based on a Reconfigurable Sliced Systolic Array","display_name":"Accelerating Convolutional Neural Network Inference Based on a Reconfigurable Sliced Systolic Array","publication_year":2021,"publication_date":"2021-04-27","ids":{"openalex":"https://openalex.org/W3158956267","doi":"https://doi.org/10.1109/iscas51556.2021.9401287","mag":"3158956267"},"language":"en","primary_location":{"id":"doi:10.1109/iscas51556.2021.9401287","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas51556.2021.9401287","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Symposium on Circuits and Systems (ISCAS)","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/A5103001149","display_name":"Yixuan Zeng","orcid":"https://orcid.org/0000-0002-2942-1859"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yixuan Zeng","raw_affiliation_strings":["State Key Laboratory of ASIC and System, Fudan University, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of ASIC and System, Fudan University, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089205282","display_name":"Heming Sun","orcid":"https://orcid.org/0000-0001-5583-4895"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Heming Sun","raw_affiliation_strings":["JST, PRESTO, Japan","Waseda Research Institute for Science and Engineering, Waseda University, Japan"],"affiliations":[{"raw_affiliation_string":"JST, PRESTO, Japan","institution_ids":[]},{"raw_affiliation_string":"Waseda Research Institute for Science and Engineering, Waseda University, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002294739","display_name":"Jiro Katto","orcid":"https://orcid.org/0000-0002-1671-2614"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jiro Katto","raw_affiliation_strings":["Graduate School of Fundamental Science and Engineering, Waseda University, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Fundamental Science and Engineering, Waseda University, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085004179","display_name":"Yibo Fan","orcid":"https://orcid.org/0000-0003-2523-8261"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yibo Fan","raw_affiliation_strings":["State Key Laboratory of ASIC and System, Fudan University, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of ASIC and System, Fudan University, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103001149"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":0.5764,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.67351307,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"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":0.9998999834060669,"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":0.9998999834060669,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9993000030517578,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9993000030517578,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7860164642333984},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7655526399612427},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7539156079292297},{"id":"https://openalex.org/keywords/dataflow","display_name":"Dataflow","score":0.6915345788002014},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.6185525059700012},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6174135208129883},{"id":"https://openalex.org/keywords/systolic-array","display_name":"Systolic array","score":0.5104514360427856},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.5090255737304688},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.4964156746864319},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42162230610847473},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.3898770213127136},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.37972426414489746},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3571317195892334},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.3423149287700653},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.2500169277191162},{"id":"https://openalex.org/keywords/very-large-scale-integration","display_name":"Very-large-scale integration","score":0.20534056425094604},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.16055569052696228},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09215712547302246}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7860164642333984},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7655526399612427},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7539156079292297},{"id":"https://openalex.org/C96324660","wikidata":"https://www.wikidata.org/wiki/Q205446","display_name":"Dataflow","level":2,"score":0.6915345788002014},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.6185525059700012},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6174135208129883},{"id":"https://openalex.org/C150741067","wikidata":"https://www.wikidata.org/wiki/Q2377218","display_name":"Systolic array","level":3,"score":0.5104514360427856},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.5090255737304688},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.4964156746864319},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42162230610847473},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.3898770213127136},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.37972426414489746},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3571317195892334},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.3423149287700653},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.2500169277191162},{"id":"https://openalex.org/C14580979","wikidata":"https://www.wikidata.org/wiki/Q876049","display_name":"Very-large-scale integration","level":2,"score":0.20534056425094604},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.16055569052696228},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09215712547302246},{"id":"https://openalex.org/C548081761","wikidata":"https://www.wikidata.org/wiki/Q180388","display_name":"Waste management","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscas51556.2021.9401287","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas51556.2021.9401287","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8700000047683716,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321851","display_name":"Fudan University","ror":"https://ror.org/013q1eq08"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2183341477","https://openalex.org/W2289252105","https://openalex.org/W2290132443","https://openalex.org/W2565960208","https://openalex.org/W2604319603","https://openalex.org/W2802238174","https://openalex.org/W2895394565","https://openalex.org/W2902593271","https://openalex.org/W2907477236","https://openalex.org/W2931301255","https://openalex.org/W2942898010","https://openalex.org/W2963122961","https://openalex.org/W2963140246","https://openalex.org/W2980852088","https://openalex.org/W6637373629"],"related_works":["https://openalex.org/W2293118914","https://openalex.org/W2998381397","https://openalex.org/W4236419692","https://openalex.org/W3167919718","https://openalex.org/W4251718783","https://openalex.org/W2171015181","https://openalex.org/W2807127337","https://openalex.org/W4391183532","https://openalex.org/W2026533826","https://openalex.org/W4283703276"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1],"networks":[2],"(CNNs)":[3],"have":[4,28],"achieved":[5],"great":[6],"successes":[7],"on":[8,57,117],"many":[9,25],"computer":[10],"vision":[11],"tasks,":[12],"such":[13],"as":[14],"image":[15],"recognition,":[16],"video":[17],"processing,":[18],"and":[19,43,77,88,121,150],"target":[20],"detection.":[21],"In":[22,35],"recent":[23],"years,":[24],"hardware":[26],"designs":[27],"been":[29],"devoted":[30],"to":[31,37,73,144],"accelerating":[32],"CNN":[33,41,111,126],"inference.":[34],"order":[36],"further":[38],"speed":[39],"up":[40],"inference":[42,127,148],"reduce":[44],"data":[45],"waste,":[46],"this":[47,91],"work":[48,92],"proposed":[49],"a":[50,94],"reconfigurable":[51],"sliced":[52],"systolic":[53],"array:":[54],"1)":[55],"Depending":[56],"the":[58,66,110,118],"number":[59],"of":[60,85],"network":[61],"nodes":[62,120],"in":[63,147],"each":[64],"layer,":[65],"slice":[67],"mode":[68],"could":[69],"be":[70],"dynamically":[71],"configured":[72],"achieve":[74],"high":[75],"throughput":[76],"resource":[78],"utilization.":[79],"2)":[80],"To":[81],"take":[82],"full":[83],"advantage":[84],"convolution":[86],"reuse":[87],"weight":[89],"reuse,":[90],"designed":[93],"tile-column":[95],"sliding":[96],"(TCS)":[97],"processing":[98],"dataflow.":[99],"3)":[100],"A":[101],"four-stage":[102],"for":[103],"loop":[104],"algorithm":[105],"was":[106],"employed,":[107],"which":[108],"divides":[109],"calculation":[112],"into":[113],"several":[114],"parts":[115],"based":[116],"input":[119],"output":[122],"nodes.":[123],"The":[124],"entire":[125],"is":[128],"carried":[129],"out":[130],"using":[131],"integer-only":[132],"arithmetic":[133],"originated":[134],"from":[135],"TensorLite.":[136],"Experimental":[137],"results":[138],"prove":[139],"that":[140],"these":[141],"strategies":[142],"lead":[143],"significant":[145],"improvement":[146],"performance":[149],"energy":[151],"efficiency.":[152]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}