{"id":"https://openalex.org/W3095494507","doi":"https://doi.org/10.1145/3424978.3424987","title":"An Optimal Design Method of Conv2d Operator for TensorFlow Based on FPGA Accelerator","display_name":"An Optimal Design Method of Conv2d Operator for TensorFlow Based on FPGA Accelerator","publication_year":2020,"publication_date":"2020-10-15","ids":{"openalex":"https://openalex.org/W3095494507","doi":"https://doi.org/10.1145/3424978.3424987","mag":"3095494507"},"language":"en","primary_location":{"id":"doi:10.1145/3424978.3424987","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3424978.3424987","pdf_url":null,"source":{"id":"https://openalex.org/S4306523839","display_name":"Proceedings of the 4th International Conference on Computer Science and Application Engineering","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th International Conference on Computer Science and Application Engineering","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/A5064453711","display_name":"Rengang Li","orcid":"https://orcid.org/0000-0002-4297-4335"},"institutions":[{"id":"https://openalex.org/I4210144143","display_name":"Inspur (China)","ror":"https://ror.org/0474p4r72","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210144143"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Rengang Li","raw_affiliation_strings":["Architecture Research Department, Inspur Electronic Information, Industry Co., Ltd, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Architecture Research Department, Inspur Electronic Information, Industry Co., Ltd, Beijing, China","institution_ids":["https://openalex.org/I4210144143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024388497","display_name":"Hongwei Kan","orcid":"https://orcid.org/0000-0002-1302-6475"},"institutions":[{"id":"https://openalex.org/I25757504","display_name":"China University of Mining and Technology","ror":"https://ror.org/01xt2dr21","country_code":"CN","type":"education","lineage":["https://openalex.org/I25757504"]},{"id":"https://openalex.org/I4210144143","display_name":"Inspur (China)","ror":"https://ror.org/0474p4r72","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210144143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongwei Kan","raw_affiliation_strings":["Inspur Electronic Information Industry Co., Ltd and AI Institute, China University of Mining and Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Inspur Electronic Information Industry Co., Ltd and AI Institute, China University of Mining and Technology, Beijing, China","institution_ids":["https://openalex.org/I25757504","https://openalex.org/I4210144143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044717081","display_name":"Dongdong Su","orcid":"https://orcid.org/0009-0005-9021-5308"},"institutions":[{"id":"https://openalex.org/I4210144143","display_name":"Inspur (China)","ror":"https://ror.org/0474p4r72","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210144143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongdong Su","raw_affiliation_strings":["Architecture Research Department, Inspur Electronic Information Industry Co., Ltd, Jinan, China"],"affiliations":[{"raw_affiliation_string":"Architecture Research Department, Inspur Electronic Information Industry Co., Ltd, Jinan, China","institution_ids":["https://openalex.org/I4210144143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100642505","display_name":"Yanwei Wang","orcid":"https://orcid.org/0000-0002-8488-9833"},"institutions":[{"id":"https://openalex.org/I4210144143","display_name":"Inspur (China)","ror":"https://ror.org/0474p4r72","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210144143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanwei Wang","raw_affiliation_strings":["Architecture Research Department, Inspur Electronic Information, Industry Co., Ltd, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Architecture Research Department, Inspur Electronic Information, Industry Co., Ltd, Beijing, China","institution_ids":["https://openalex.org/I4210144143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011270130","display_name":"Hongbo Zhao","orcid":"https://orcid.org/0000-0002-1196-4089"},"institutions":[{"id":"https://openalex.org/I4210144143","display_name":"Inspur (China)","ror":"https://ror.org/0474p4r72","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210144143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongbo Zhao","raw_affiliation_strings":["Architecture Research Department, Inspur Electronic Information, Industry Co., Ltd, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Architecture Research Department, Inspur Electronic Information, Industry Co., Ltd, Beijing, China","institution_ids":["https://openalex.org/I4210144143"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050562058","display_name":"Peilin Tong","orcid":null},"institutions":[{"id":"https://openalex.org/I4210144143","display_name":"Inspur (China)","ror":"https://ror.org/0474p4r72","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210144143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peilin Tong","raw_affiliation_strings":["Architecture Research Department, Inspur Electronic Information, Industry Co., Ltd, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Architecture Research Department, Inspur Electronic Information, Industry Co., Ltd, Beijing, China","institution_ids":["https://openalex.org/I4210144143"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5064453711"],"corresponding_institution_ids":["https://openalex.org/I4210144143"],"apc_list":null,"apc_paid":null,"fwci":0.4623,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.52729398,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9991000294685364,"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":0.9991000294685364,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9976999759674072,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"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":0.8121050596237183},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.7946093082427979},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.7572168111801147},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.7275646328926086},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.6427358388900757},{"id":"https://openalex.org/keywords/hardware-acceleration","display_name":"Hardware acceleration","score":0.4972999393939972},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.4622023105621338},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4320053458213806},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.3263544738292694},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32450252771377563},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3052075207233429}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8121050596237183},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.7946093082427979},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.7572168111801147},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.7275646328926086},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.6427358388900757},{"id":"https://openalex.org/C13164978","wikidata":"https://www.wikidata.org/wiki/Q600158","display_name":"Hardware acceleration","level":3,"score":0.4972999393939972},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.4622023105621338},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4320053458213806},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.3263544738292694},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32450252771377563},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3052075207233429},{"id":"https://openalex.org/C86339819","wikidata":"https://www.wikidata.org/wiki/Q407384","display_name":"Transcription factor","level":3,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C158448853","wikidata":"https://www.wikidata.org/wiki/Q425218","display_name":"Repressor","level":4,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3424978.3424987","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3424978.3424987","pdf_url":null,"source":{"id":"https://openalex.org/S4306523839","display_name":"Proceedings of the 4th International Conference on Computer Science and Application Engineering","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th International Conference on Computer Science and Application Engineering","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":15,"referenced_works":["https://openalex.org/W2585560244","https://openalex.org/W2621550233","https://openalex.org/W2727238169","https://openalex.org/W2748584437","https://openalex.org/W2786845740","https://openalex.org/W2901848761","https://openalex.org/W2902084433","https://openalex.org/W2907317246","https://openalex.org/W2914039041","https://openalex.org/W2938991217","https://openalex.org/W2949470719","https://openalex.org/W2984895873","https://openalex.org/W2987548521","https://openalex.org/W6756014915","https://openalex.org/W6761678230"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W2750384547","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W4285144618","https://openalex.org/W2518118925","https://openalex.org/W4285104150","https://openalex.org/W3159273459"],"abstract_inverted_index":{"Currently,":[0],"TensorFlow":[1,30,111,253],"architecture":[2],"only":[3,196],"supports":[4],"CPU":[5,189],"and":[6,9,75,95,105,113,140,161,190],"GPU":[7],"programming,":[8],"has":[10],"not":[11,51],"yet":[12],"formed":[13],"a":[14,32],"unified":[15],"support":[16],"standard":[17],"for":[18,67,79,89,109,252,258],"FPGAs.":[19],"To":[20],"the":[21,35,40,46,103,114,126,136,141,159,166,171,178,181,192,203,211,217,228,235,242],"best":[22],"of":[23,125,130,170,180,205,213,220],"our":[24],"knowledge,":[25],"when":[26,227],"forward":[27,94,160],"operators":[28,97,163,257],"in":[29,39,59,165,202,216],"specifies":[31],"new":[33],"device,":[34,48],"backward":[36,96,162],"gradient":[37],"operator":[38,81,116,215],"same":[41,47],"neural":[42,259],"network":[43,260],"cannot":[44],"use":[45,255],"which":[49],"does":[50],"comply":[52],"with":[53,188],"rules":[54],"about":[55,197],"node":[56,68,90,104],"device":[57,69,91,106],"allocation":[58,70,92,107],"TensorFlow.":[60],"Therefore,":[61],"we":[62,156,209,247],"propose":[63],"an":[64,76,249],"improved":[65,87],"algorithm":[66,78,88,118],"based":[71,82,98,119,152],"on":[72,83,99,120,153],"placement":[73],"mechanism":[74],"optimization":[77,117,250],"conv2d":[80,115,150,214],"OpenCL.":[84],"The":[85,173,223],"proposed":[86,248],"makes":[93],"FPGA":[100,154,236,256],"accelerator":[101],"satisfy":[102],"requirements":[108],"all":[110],"operators,":[112],"OpenCL":[121],"takes":[122],"full":[123],"advantage":[124],"parallel":[127],"computing":[128],"advantages":[129],"FPGA.":[131],"Finally,":[132],"this":[133,221],"paper":[134],"uses":[135],"CNN":[137],"LeNet5":[138],"model":[139],"MNIST":[142],"dataset":[143],"to":[144,149,233,254],"conduct":[145],"corresponding":[146],"experiments.":[147],"Referring":[148],"operator,":[151],"accelerator,":[155],"implement":[157],"both":[158],"involved":[164],"first":[167,218],"four":[168],"layers":[169],"model.":[172,222],"experimental":[174],"results":[175,224],"show":[176,225],"that":[177,226,246],"accuracy":[179,193],"three":[182],"methods":[183],"is":[184,195],"above":[185],"98%.":[186],"Compared":[187],"GPU,":[191],"difference":[194],"five":[198],"thousandths.":[199],"In":[200],"addition,":[201],"case":[204],"different":[206],"batch":[207,230],"sizes,":[208],"tested":[210],"runtime":[212],"layer":[219],"input":[229],"size":[231],"increased":[232],"10000,":[234],"runs":[237],"9":[238],"times":[239],"faster":[240],"than":[241],"CPU.":[243],"It":[244],"proved":[245],"solution":[251],"calculations.":[261]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
