{"id":"https://openalex.org/W4282008392","doi":"https://doi.org/10.1109/fccm53951.2022.9786215","title":"Dual-Line-Systolic Array for High Performance CNN Accelerator","display_name":"Dual-Line-Systolic Array for High Performance CNN Accelerator","publication_year":2022,"publication_date":"2022-05-15","ids":{"openalex":"https://openalex.org/W4282008392","doi":"https://doi.org/10.1109/fccm53951.2022.9786215"},"language":"en","primary_location":{"id":"doi:10.1109/fccm53951.2022.9786215","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fccm53951.2022.9786215","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 30th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)","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/A5034580482","display_name":"Peng Xue","orcid":"https://orcid.org/0000-0002-4272-2883"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Peng Xue","raw_affiliation_strings":["Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences"],"affiliations":[{"raw_affiliation_string":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087505067","display_name":"Lunshuai Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lunshuai Pan","raw_affiliation_strings":["Southeast University"],"affiliations":[{"raw_affiliation_string":"Southeast University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009847938","display_name":"Litao Sun","orcid":"https://orcid.org/0000-0002-2750-5004"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Litao Sun","raw_affiliation_strings":["Southeast University"],"affiliations":[{"raw_affiliation_string":"Southeast University","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038495391","display_name":"Mingqiang Huang","orcid":"https://orcid.org/0000-0002-7794-3985"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingqiang Huang","raw_affiliation_strings":["Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences"],"affiliations":[{"raw_affiliation_string":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences","institution_ids":["https://openalex.org/I4210145761","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5034580482"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210145761"],"apc_list":null,"apc_paid":null,"fwci":1.0076,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.76480674,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9753000140190125,"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":0.9753000140190125,"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/T13182","display_name":"Quantum-Dot Cellular Automata","score":0.973800003528595,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/systolic-array","display_name":"Systolic array","score":0.8827662467956543},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.741525411605835},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7029722929000854},{"id":"https://openalex.org/keywords/dataflow","display_name":"Dataflow","score":0.6907163858413696},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.6389283537864685},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.6176729798316956},{"id":"https://openalex.org/keywords/digital-signal-processing","display_name":"Digital signal processing","score":0.5430689454078674},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.49752429127693176},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.47102317214012146},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.4377892315387726},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.43221551179885864},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.392729789018631},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.31283706426620483},{"id":"https://openalex.org/keywords/very-large-scale-integration","display_name":"Very-large-scale integration","score":0.2787032723426819},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17017093300819397},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08858153223991394}],"concepts":[{"id":"https://openalex.org/C150741067","wikidata":"https://www.wikidata.org/wiki/Q2377218","display_name":"Systolic array","level":3,"score":0.8827662467956543},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.741525411605835},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7029722929000854},{"id":"https://openalex.org/C96324660","wikidata":"https://www.wikidata.org/wiki/Q205446","display_name":"Dataflow","level":2,"score":0.6907163858413696},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.6389283537864685},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.6176729798316956},{"id":"https://openalex.org/C84462506","wikidata":"https://www.wikidata.org/wiki/Q173142","display_name":"Digital signal processing","level":2,"score":0.5430689454078674},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.49752429127693176},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.47102317214012146},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.4377892315387726},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.43221551179885864},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.392729789018631},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.31283706426620483},{"id":"https://openalex.org/C14580979","wikidata":"https://www.wikidata.org/wiki/Q876049","display_name":"Very-large-scale integration","level":2,"score":0.2787032723426819},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17017093300819397},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08858153223991394},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fccm53951.2022.9786215","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fccm53951.2022.9786215","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 30th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":1,"referenced_works":["https://openalex.org/W3119659890"],"related_works":["https://openalex.org/W2293118914","https://openalex.org/W2998381397","https://openalex.org/W4236419692","https://openalex.org/W3167919718","https://openalex.org/W2999668243","https://openalex.org/W1876592433","https://openalex.org/W2083269738","https://openalex.org/W2807127337","https://openalex.org/W2347854075","https://openalex.org/W2169963286"],"abstract_inverted_index":{"Systolic":[0],"array":[1,33,77],"has":[2,116],"been":[3],"the":[4,14,36,41,46,54,67,82,89,105,120,124],"crucial":[5],"architecture":[6],"for":[7,34,96],"accelerating":[8,35,98],"convolutional":[9,37],"neural":[10,38,100],"networks":[11],"(CNN)":[12],"since":[13],"success":[15],"of":[16,66,104,119],"Google\u2019s":[17],"TPU":[18],"(Tensor":[19],"Processing":[20],"Unit).":[21],"In":[22],"this":[23],"work,":[24],"we":[25],"propose":[26],"high":[27,111],"throughput":[28,84,103],"and":[29,53],"low":[30],"delay":[31,56],"dual-line-systolic":[32,76],"networks.":[39],"With":[40],"line-by-line":[42],"vector-style":[43],"systolic":[44],"dataflow,":[45],"peripheral":[47],"circuit":[48],"can":[49,57,85,108],"be":[50,58,86],"well":[51],"simplified":[52],"loading/offloading":[55],"greatly":[59],"reduced.":[60],"Besides,":[61],"to":[62],"fully":[63],"take":[64],"advantage":[65],"DSP":[68],"(Digital":[69],"signal":[70],"processor)":[71],"INT8":[72],"computation":[73,83],"in":[74],"FPGA,":[75],"is":[78,92],"developed,":[79],"by":[80],"which":[81,115],"doubled.":[87],"Finally,":[88],"proposed":[90],"accelerator":[91],"deployed":[93],"on":[94,123],"PYNQ-Z2":[95],"practically":[97],"VGG16":[99],"network,":[101],"peek":[102],"convolution":[106],"layer":[107],"reach":[109],"as":[110,112],"107.21":[113],"GOPS,":[114],"exceeded":[117],"all":[118],"previous":[121],"works":[122],"same":[125],"hardware":[126],"platform.":[127]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"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"}