{"id":"https://openalex.org/W3127984510","doi":"https://doi.org/10.1109/isocc50952.2020.9333049","title":"Prototype of Low Complexity CNN Hardware Accelerator with FPGA-based PYNQ Platform for Dual-Mode Biometrics Recognition","display_name":"Prototype of Low Complexity CNN Hardware Accelerator with FPGA-based PYNQ Platform for Dual-Mode Biometrics Recognition","publication_year":2020,"publication_date":"2020-10-21","ids":{"openalex":"https://openalex.org/W3127984510","doi":"https://doi.org/10.1109/isocc50952.2020.9333049","mag":"3127984510"},"language":"en","primary_location":{"id":"doi:10.1109/isocc50952.2020.9333049","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isocc50952.2020.9333049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International SoC Design Conference (ISOCC)","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/A5101623682","display_name":"Yu-Hsiang Chen","orcid":"https://orcid.org/0000-0002-7150-6741"},"institutions":[{"id":"https://openalex.org/I162838928","display_name":"National Chung Hsing University","ror":"https://ror.org/05vn3ca78","country_code":"TW","type":"education","lineage":["https://openalex.org/I162838928"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yu-Hsiang Chen","raw_affiliation_strings":["Department of Electrical Engineering, National Chung Hsing University, Taichung city, Taiwan (R.O.C.)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Chung Hsing University, Taichung city, Taiwan (R.O.C.)","institution_ids":["https://openalex.org/I162838928"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003025198","display_name":"Chih\u2010Peng Fan","orcid":"https://orcid.org/0000-0002-0585-975X"},"institutions":[{"id":"https://openalex.org/I162838928","display_name":"National Chung Hsing University","ror":"https://ror.org/05vn3ca78","country_code":"TW","type":"education","lineage":["https://openalex.org/I162838928"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chih-Peng Fan","raw_affiliation_strings":["Department of Electrical Engineering, National Chung Hsing University, Taichung city, Taiwan (R.O.C.)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Chung Hsing University, Taichung city, Taiwan (R.O.C.)","institution_ids":["https://openalex.org/I162838928"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079948174","display_name":"KyungHi Chang","orcid":"https://orcid.org/0000-0002-3233-8087"},"institutions":[{"id":"https://openalex.org/I162838928","display_name":"National Chung Hsing University","ror":"https://ror.org/05vn3ca78","country_code":"TW","type":"education","lineage":["https://openalex.org/I162838928"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Robert Chen-Hao Chang","raw_affiliation_strings":["Department of Electrical Engineering, National Chung Hsing University, Taichung city, Taiwan (R.O.C.)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Chung Hsing University, Taichung city, Taiwan (R.O.C.)","institution_ids":["https://openalex.org/I162838928"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I162838928"],"apc_list":null,"apc_paid":null,"fwci":1.9718,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.88723051,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10828","display_name":"Biometric Identification and Security","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10057","display_name":"Face and Expression Recognition","score":0.9330000281333923,"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/T11800","display_name":"User Authentication and Security Systems","score":0.9271000027656555,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/computer-science","display_name":"Computer science","score":0.82622230052948},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.8203339576721191},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.754380464553833},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.6974867582321167},{"id":"https://openalex.org/keywords/hardware-acceleration","display_name":"Hardware acceleration","score":0.6078957319259644},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5468412041664124},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5051248669624329},{"id":"https://openalex.org/keywords/dual-mode","display_name":"Dual mode","score":0.47003787755966187},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.45815977454185486},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.4500567615032196},{"id":"https://openalex.org/keywords/iris-recognition","display_name":"Iris recognition","score":0.4437849819660187},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37455812096595764},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3731759786605835},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.3390103578567505}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.82622230052948},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.8203339576721191},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.754380464553833},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.6974867582321167},{"id":"https://openalex.org/C13164978","wikidata":"https://www.wikidata.org/wiki/Q600158","display_name":"Hardware acceleration","level":3,"score":0.6078957319259644},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5468412041664124},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5051248669624329},{"id":"https://openalex.org/C3019325349","wikidata":"https://www.wikidata.org/wiki/Q3874753","display_name":"Dual mode","level":2,"score":0.47003787755966187},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.45815977454185486},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.4500567615032196},{"id":"https://openalex.org/C112356035","wikidata":"https://www.wikidata.org/wiki/Q1672722","display_name":"Iris recognition","level":3,"score":0.4437849819660187},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37455812096595764},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3731759786605835},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3390103578567505},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace 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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isocc50952.2020.9333049","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isocc50952.2020.9333049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International SoC Design Conference (ISOCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6939007800","display_name":null,"funder_award_id":"MOST 108-2218-E-005-017","funder_id":"https://openalex.org/F4320309618","funder_display_name":"Ministry of Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320309618","display_name":"Ministry of Science and Technology","ror":"https://ror.org/02b207r52"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2025065882","https://openalex.org/W2139695910","https://openalex.org/W2807156154","https://openalex.org/W2885228218","https://openalex.org/W6680962012"],"related_works":["https://openalex.org/W2018223046","https://openalex.org/W2294693339","https://openalex.org/W1971649232","https://openalex.org/W2573408845","https://openalex.org/W2774310452","https://openalex.org/W231692020","https://openalex.org/W3092387234","https://openalex.org/W2024700913","https://openalex.org/W2182784357","https://openalex.org/W2518118925"],"abstract_inverted_index":{"In":[0],"this":[1],"study,":[2],"the":[3,15,24,27,32,51,58,73,79,86,91,95,102,108,113,121,126,136],"effective":[4],"low-complexity":[5],"convolutional":[6],"neural":[7],"network":[8,11],"(CNN)":[9],"inference":[10],"is":[12],"implemented":[13],"by":[14,66,131],"FPGA-based":[16],"hardware":[17,69,117],"accelerator":[18],"for":[19,47,68],"dual-mode":[20],"biometric":[21],"authentications.":[22],"After":[23],"pre-processing":[25],"processes,":[26],"selected":[28],"datasets,":[29],"which":[30],"include":[31],"finger":[33,103],"vein":[34,104],"images":[35,38],"and":[36,42,49,125],"eye":[37,88],"with":[39,85,101,107],"partial":[40],"iris":[41],"sclera":[43],"zones,":[44],"are":[45,129],"used":[46],"training":[48],"testing":[50],"LeNet-5":[52],"based":[53,111],"CNN":[54,60,76,116],"lite":[55,59,75,115],"model.":[56],"Then":[57],"classifier":[61],"will":[62],"be":[63],"rapidly":[64],"prototyped":[65],"FPGA":[67,138],"acceleration.":[70],"By":[71],"tests,":[72],"proposed":[74,92,114],"model":[77,93],"achieves":[78,94],"recognition":[80,96,123],"accuracy":[81,97],"up":[82,98],"to":[83,99],"97%":[84],"ROI-based":[87],"images.":[89,105],"Besides,":[90],"95%":[100],"Compared":[106],"pure":[109],"software":[110],"implementation,":[112],"acceleration":[118],"design":[119],"provides":[120],"same":[122],"accuracy,":[124],"inferential":[127],"calculations":[128],"accelerated":[130],"about":[132],"50":[133],"times":[134],"on":[135],"PYNQ":[137],"platform.":[139]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}