{"id":"https://openalex.org/W4323831447","doi":"https://doi.org/10.1109/iceic57457.2023.10049910","title":"Quantitative Analysis of Various 2D CNN Structures based on Dataflow","display_name":"Quantitative Analysis of Various 2D CNN Structures based on Dataflow","publication_year":2023,"publication_date":"2023-02-05","ids":{"openalex":"https://openalex.org/W4323831447","doi":"https://doi.org/10.1109/iceic57457.2023.10049910"},"language":"en","primary_location":{"id":"doi:10.1109/iceic57457.2023.10049910","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iceic57457.2023.10049910","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Electronics, Information, and Communication (ICEIC)","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/A5100360920","display_name":"Sangwon Lee","orcid":"https://orcid.org/0000-0003-3936-9342"},"institutions":[{"id":"https://openalex.org/I196345858","display_name":"Chungnam National University","ror":"https://ror.org/0227as991","country_code":"KR","type":"education","lineage":["https://openalex.org/I196345858"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Sangwon Lee","raw_affiliation_strings":["Chungnam National University,Dept. of Electronics Engineering,Daejeon,Korea"],"affiliations":[{"raw_affiliation_string":"Chungnam National University,Dept. of Electronics Engineering,Daejeon,Korea","institution_ids":["https://openalex.org/I196345858"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100599672","display_name":"Jiho Park","orcid":"https://orcid.org/0000-0002-4570-324X"},"institutions":[{"id":"https://openalex.org/I196345858","display_name":"Chungnam National University","ror":"https://ror.org/0227as991","country_code":"KR","type":"education","lineage":["https://openalex.org/I196345858"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jiho Park","raw_affiliation_strings":["Chungnam National University,Dept. of Electronics Engineering,Daejeon,Korea"],"affiliations":[{"raw_affiliation_string":"Chungnam National University,Dept. of Electronics Engineering,Daejeon,Korea","institution_ids":["https://openalex.org/I196345858"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100396225","display_name":"Jeongho Kim","orcid":"https://orcid.org/0000-0002-5220-3139"},"institutions":[{"id":"https://openalex.org/I196345858","display_name":"Chungnam National University","ror":"https://ror.org/0227as991","country_code":"KR","type":"education","lineage":["https://openalex.org/I196345858"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jeongho Kim","raw_affiliation_strings":["Chungnam National University,Dept. of Electronics Engineering,Daejeon,Korea"],"affiliations":[{"raw_affiliation_string":"Chungnam National University,Dept. of Electronics Engineering,Daejeon,Korea","institution_ids":["https://openalex.org/I196345858"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100846792","display_name":"Yongtaek Hwang","orcid":null},"institutions":[{"id":"https://openalex.org/I196345858","display_name":"Chungnam National University","ror":"https://ror.org/0227as991","country_code":"KR","type":"education","lineage":["https://openalex.org/I196345858"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yongtaek Hwang","raw_affiliation_strings":["Chungnam National University,Dept. of Electronics Engineering,Daejeon,Korea"],"affiliations":[{"raw_affiliation_string":"Chungnam National University,Dept. of Electronics Engineering,Daejeon,Korea","institution_ids":["https://openalex.org/I196345858"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101467591","display_name":"So\u2010Yeon Choi","orcid":"https://orcid.org/0000-0001-8299-7892"},"institutions":[{"id":"https://openalex.org/I196345858","display_name":"Chungnam National University","ror":"https://ror.org/0227as991","country_code":"KR","type":"education","lineage":["https://openalex.org/I196345858"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Soyeon Choi","raw_affiliation_strings":["Chungnam National University,Dept. of Electronics Engineering,Daejeon,Korea"],"affiliations":[{"raw_affiliation_string":"Chungnam National University,Dept. of Electronics Engineering,Daejeon,Korea","institution_ids":["https://openalex.org/I196345858"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087010497","display_name":"Hoyoung Yoo","orcid":"https://orcid.org/0000-0001-9323-0398"},"institutions":[{"id":"https://openalex.org/I196345858","display_name":"Chungnam National University","ror":"https://ror.org/0227as991","country_code":"KR","type":"education","lineage":["https://openalex.org/I196345858"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hoyoung Yoo","raw_affiliation_strings":["Chungnam National University,Dept. of Electronics Engineering,Daejeon,Korea"],"affiliations":[{"raw_affiliation_string":"Chungnam National University,Dept. of Electronics Engineering,Daejeon,Korea","institution_ids":["https://openalex.org/I196345858"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100360920"],"corresponding_institution_ids":["https://openalex.org/I196345858"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01504447,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"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":0.9980000257492065,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/dataflow","display_name":"Dataflow","score":0.9727561473846436},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8726339340209961},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.797377347946167},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7965120077133179},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.68843674659729},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5078111290931702},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.45912325382232666},{"id":"https://openalex.org/keywords/cellular-neural-network","display_name":"Cellular neural network","score":0.4442735016345978},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44374990463256836},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4364508092403412},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4217827618122101},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.383064866065979},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.34348171949386597},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3426463007926941},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09894588589668274}],"concepts":[{"id":"https://openalex.org/C96324660","wikidata":"https://www.wikidata.org/wiki/Q205446","display_name":"Dataflow","level":2,"score":0.9727561473846436},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8726339340209961},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.797377347946167},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7965120077133179},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.68843674659729},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5078111290931702},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.45912325382232666},{"id":"https://openalex.org/C812465","wikidata":"https://www.wikidata.org/wiki/Q5058375","display_name":"Cellular neural network","level":3,"score":0.4442735016345978},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44374990463256836},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4364508092403412},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4217827618122101},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.383064866065979},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.34348171949386597},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3426463007926941},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09894588589668274},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"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/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iceic57457.2023.10049910","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iceic57457.2023.10049910","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Electronics, Information, and Communication (ICEIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8999999761581421}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320322202","display_name":"IC Design Education Center","ror":"https://ror.org/005v57z85"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1005811612","https://openalex.org/W2289252105","https://openalex.org/W2593564159","https://openalex.org/W2618530766","https://openalex.org/W2807127337"],"related_works":["https://openalex.org/W4312417841","https://openalex.org/W3156786002","https://openalex.org/W2738221750","https://openalex.org/W2732542196","https://openalex.org/W564581980","https://openalex.org/W3129634582","https://openalex.org/W4323831447","https://openalex.org/W3165266428","https://openalex.org/W4293794540","https://openalex.org/W4312402255"],"abstract_inverted_index":{"Convolutional":[0],"Neural":[1],"Networks":[2],"(CNNs)":[3],"are":[4,41,86],"used":[5],"in":[6,28,90,121],"a":[7,49,57,69,106],"wide":[8],"range":[9],"of":[10,81,92,114],"fields":[11],"due":[12],"to":[13],"their":[14],"excellent":[15],"accuracy.":[16],"Previous":[17],"researchers":[18],"have":[19],"proposed":[20],"convolution":[21],"architectures":[22],"for":[23,75,128],"the":[24,35,62,108,115,123],"typical":[25],"convolutional":[26,51],"layer":[27,52],"CNN":[29,77,84,117],"whose":[30],"input":[31],"is":[32,48],"larger":[33,58],"than":[34,61],"kernel":[36,59],"size.":[37],"However,":[38],"neural":[39,130],"networks":[40],"continuously":[42],"evolving":[43],"and":[44,46,88,97,112,119],"developing,":[45],"there":[47],"deep":[50],"unlike":[53],"classic":[54],"CNNs":[55],"demands":[56],"size":[60],"input.":[63],"In":[64],"this":[65],"paper,":[66],"we":[67],"conduct":[68],"quantitative":[70],"analysis":[71],"based":[72,100],"on":[73,101],"dataflow":[74,103],"various":[76,129],"structures.":[78],"A":[79],"total":[80,95],"eight":[82],"2D":[83],"structures":[85,118],"described":[87],"compared":[89],"terms":[91],"processing":[93],"time,":[94],"area,":[96],"energy":[98],"efficiency,":[99],"different":[102,116],"graphs.":[104],"As":[105],"result,":[107],"comparison":[109],"provides":[110],"advantages":[111],"disadvantages":[113],"aids":[120],"determining":[122],"optimal":[124],"hardware":[125],"structure":[126],"solution":[127],"network":[131],"types.":[132]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}