{"id":"https://openalex.org/W2793980757","doi":"https://doi.org/10.1109/tcsi.2018.2804946","title":"A Modular and Reconfigurable Pipeline Architecture for Learning Vector Quantization","display_name":"A Modular and Reconfigurable Pipeline Architecture for Learning Vector Quantization","publication_year":2018,"publication_date":"2018-02-23","ids":{"openalex":"https://openalex.org/W2793980757","doi":"https://doi.org/10.1109/tcsi.2018.2804946","mag":"2793980757"},"language":"en","primary_location":{"id":"doi:10.1109/tcsi.2018.2804946","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsi.2018.2804946","pdf_url":null,"source":{"id":"https://openalex.org/S116977442","display_name":"IEEE Transactions on Circuits and Systems I Regular Papers","issn_l":"1549-8328","issn":["1549-8328","1558-0806"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Circuits and Systems I: Regular Papers","raw_type":"journal-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/A5100362471","display_name":"Xiangyu Zhang","orcid":"https://orcid.org/0000-0003-3716-4722"},"institutions":[{"id":"https://openalex.org/I113306721","display_name":"Hiroshima University","ror":"https://ror.org/03t78wx29","country_code":"JP","type":"education","lineage":["https://openalex.org/I113306721"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xiangyu Zhang","raw_affiliation_strings":["Graduate School of Engineering, Hiroshima University, Higashihiroshima, Japan"],"raw_orcid":"https://orcid.org/0000-0003-3716-4722","affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, Hiroshima University, Higashihiroshima, Japan","institution_ids":["https://openalex.org/I113306721"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069123107","display_name":"Fengwei An","orcid":"https://orcid.org/0000-0002-7554-7938"},"institutions":[{"id":"https://openalex.org/I113306721","display_name":"Hiroshima University","ror":"https://ror.org/03t78wx29","country_code":"JP","type":"education","lineage":["https://openalex.org/I113306721"]},{"id":"https://openalex.org/I4210126469","display_name":"Higashihiroshima Medical Center","ror":"https://ror.org/03bd22t26","country_code":"JP","type":"healthcare","lineage":["https://openalex.org/I4210126469","https://openalex.org/I4210137409"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Fengwei An","raw_affiliation_strings":["Institute of Engineering, Hiroshima University, HigashiHiroshima, Japan"],"raw_orcid":"https://orcid.org/0000-0002-7554-7938","affiliations":[{"raw_affiliation_string":"Institute of Engineering, Hiroshima University, HigashiHiroshima, Japan","institution_ids":["https://openalex.org/I113306721","https://openalex.org/I4210126469"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100671215","display_name":"Lei Chen","orcid":"https://orcid.org/0000-0001-9369-9524"},"institutions":[{"id":"https://openalex.org/I113306721","display_name":"Hiroshima University","ror":"https://ror.org/03t78wx29","country_code":"JP","type":"education","lineage":["https://openalex.org/I113306721"]},{"id":"https://openalex.org/I4210126469","display_name":"Higashihiroshima Medical Center","ror":"https://ror.org/03bd22t26","country_code":"JP","type":"healthcare","lineage":["https://openalex.org/I4210126469","https://openalex.org/I4210137409"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Lei Chen","raw_affiliation_strings":["HiSIM Research Center, Hiroshima University, HigashiHiroshima, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"HiSIM Research Center, Hiroshima University, HigashiHiroshima, Japan","institution_ids":["https://openalex.org/I113306721","https://openalex.org/I4210126469"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060719224","display_name":"Idaku Ishii","orcid":"https://orcid.org/0000-0002-7728-2363"},"institutions":[{"id":"https://openalex.org/I113306721","display_name":"Hiroshima University","ror":"https://ror.org/03t78wx29","country_code":"JP","type":"education","lineage":["https://openalex.org/I113306721"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Idaku Ishii","raw_affiliation_strings":["Graduate School of Engineering, Hiroshima University, Higashihiroshima, Japan"],"raw_orcid":"https://orcid.org/0000-0002-7728-2363","affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, Hiroshima University, Higashihiroshima, Japan","institution_ids":["https://openalex.org/I113306721"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086404781","display_name":"Hans J\u00fcrgen Mattausch","orcid":"https://orcid.org/0000-0001-5712-1020"},"institutions":[{"id":"https://openalex.org/I113306721","display_name":"Hiroshima University","ror":"https://ror.org/03t78wx29","country_code":"JP","type":"education","lineage":["https://openalex.org/I113306721"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hans Jurgen Mattausch","raw_affiliation_strings":["Research Institute for Nanodevice and Bio Systems, Hiroshima University, HigashiHiroshima, Japan"],"raw_orcid":"https://orcid.org/0000-0001-5712-1020","affiliations":[{"raw_affiliation_string":"Research Institute for Nanodevice and Bio Systems, Hiroshima University, HigashiHiroshima, Japan","institution_ids":["https://openalex.org/I113306721"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4938,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.63121701,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"65","issue":"10","first_page":"3312","last_page":"3325"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9990000128746033,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9990000128746033,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10320","display_name":"Neural Networks and Applications","score":0.9904000163078308,"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/learning-vector-quantization","display_name":"Learning vector quantization","score":0.9282711744308472},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.7362872958183289},{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.7070749402046204},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6779789328575134},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.635176420211792},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.4509241282939911},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.44214481115341187},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.44028547406196594},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4304269552230835},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.424185574054718},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4160114526748657},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.3846094012260437},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35650837421417236},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3313741087913513},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.13888677954673767},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.0878078043460846},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07495135068893433}],"concepts":[{"id":"https://openalex.org/C40567965","wikidata":"https://www.wikidata.org/wiki/Q1820283","display_name":"Learning vector quantization","level":3,"score":0.9282711744308472},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.7362872958183289},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.7070749402046204},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6779789328575134},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.635176420211792},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.4509241282939911},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.44214481115341187},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.44028547406196594},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4304269552230835},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.424185574054718},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4160114526748657},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.3846094012260437},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35650837421417236},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3313741087913513},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.13888677954673767},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0878078043460846},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07495135068893433},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsi.2018.2804946","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsi.2018.2804946","pdf_url":null,"source":{"id":"https://openalex.org/S116977442","display_name":"IEEE Transactions on Circuits and Systems I Regular Papers","issn_l":"1549-8328","issn":["1549-8328","1558-0806"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Circuits and Systems I: Regular Papers","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5296922459","display_name":"Mulithread High-speed Vision Sensing Using Ultrafast Active Vision","funder_award_id":"16H02348","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G7723878923","display_name":"\u30de\u30eb\u30c1\u30dd\u30fc\u30c8\u30fb\u30e1\u30e2\u30ea\u30fb\u30d9\u30fc\u30b9\u306e\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306b\u95a2\u3059\u308b\u7814\u7a76","funder_award_id":"17K14668","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320320912","display_name":"Ministry of Education, Culture, Sports, Science and Technology","ror":"https://ror.org/048rj2z13"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W17851263","https://openalex.org/W65738273","https://openalex.org/W85703684","https://openalex.org/W128268289","https://openalex.org/W333278977","https://openalex.org/W1524093576","https://openalex.org/W1541817687","https://openalex.org/W1555344148","https://openalex.org/W1562216132","https://openalex.org/W1584356919","https://openalex.org/W1980889170","https://openalex.org/W1982606474","https://openalex.org/W1990163358","https://openalex.org/W1990863955","https://openalex.org/W2008802978","https://openalex.org/W2033690918","https://openalex.org/W2041582537","https://openalex.org/W2066922565","https://openalex.org/W2091006540","https://openalex.org/W2093618981","https://openalex.org/W2109808436","https://openalex.org/W2114125949","https://openalex.org/W2133092215","https://openalex.org/W2140033388","https://openalex.org/W2142832068","https://openalex.org/W2153952995","https://openalex.org/W2277469218","https://openalex.org/W2290848168","https://openalex.org/W2339627369","https://openalex.org/W2484903931","https://openalex.org/W2494942050","https://openalex.org/W2535626799","https://openalex.org/W2537705861","https://openalex.org/W2590112380","https://openalex.org/W2601355328","https://openalex.org/W2620550069","https://openalex.org/W2734524013","https://openalex.org/W3004531124","https://openalex.org/W3141434431","https://openalex.org/W6600756527","https://openalex.org/W6634700816","https://openalex.org/W6640443432","https://openalex.org/W6679694798","https://openalex.org/W6728383536"],"related_works":["https://openalex.org/W2743872637","https://openalex.org/W1990646304","https://openalex.org/W2100492357","https://openalex.org/W3193872944","https://openalex.org/W1805748654","https://openalex.org/W2106346348","https://openalex.org/W3148658660","https://openalex.org/W1559148705","https://openalex.org/W3049633467","https://openalex.org/W2021891987"],"abstract_inverted_index":{"Learning":[0],"vector":[1,68],"quantization":[2],"(LVQ)":[3],"neural":[4],"networks":[5],"have":[6],"already":[7],"been":[8],"successfully":[9],"applied":[10],"for":[11,29,45,119,127],"image":[12],"compression":[13],"and":[14,24,38,42,47,59,64,73,95,108,121],"object":[15],"recognition.":[16],"In":[17],"this":[18],"paper,":[19],"we":[20],"propose":[21],"a":[22,40],"modular":[23],"reconfigurable":[25,36],"pipeline":[26],"architecture":[27],"(MRPA)":[28],"LVQ.":[30],"The":[31,98],"MRPA":[32,99],"consists":[33],"of":[34,71,92,105],"dynamically":[35],"modules":[37],"realizes":[39],"run-time":[41],"on-chip":[43],"configuration":[44],"recognition":[46,94,120],"learning.":[48],"Prototype":[49],"fabrication":[50],"in":[51,67],"65-nm":[52],"CMOS":[53],"technology":[54],"verifies":[55],"high":[56],"integration":[57],"density":[58],"memory-utilization":[60],"efficiency,":[61],"good":[62],"performance,":[63],"considerable":[65],"flexibility":[66],"dimensionality,":[69],"number":[70],"weight-vectors,":[72],"adaption":[74],"strategies.":[75],"Compared":[76],"with":[77],"the":[78,86,90,111],"embedded":[79],"microprocessors,":[80],"which":[81],"rely":[82],"on":[83,110],"single-instruction-multiple-data":[84],"processing,":[85],"developed":[87],"prototype":[88,100],"increases":[89],"performance":[91,113],"both":[93],"learning":[96],"operations.":[97],"shows":[101],"improvements":[102],"by":[103],"factors":[104],"approximately":[106],"40":[107],"101":[109],"well-established":[112],"metrics":[114],"million":[115,122],"connections":[116],"per":[117,125],"second":[118,126],"connection":[123],"updates":[124],"learning,":[128],"respectively.":[129]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
