{"id":"https://openalex.org/W2958745039","doi":"https://doi.org/10.1109/ismvl.2019.00023","title":"Noise Convolutional Neural Networks and FPGA Implementation","display_name":"Noise Convolutional Neural Networks and FPGA Implementation","publication_year":2019,"publication_date":"2019-05-01","ids":{"openalex":"https://openalex.org/W2958745039","doi":"https://doi.org/10.1109/ismvl.2019.00023","mag":"2958745039"},"language":"en","primary_location":{"id":"doi:10.1109/ismvl.2019.00023","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ismvl.2019.00023","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 49th International Symposium on Multiple-Valued Logic (ISMVL)","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/A5069635518","display_name":"Atsuki Munakata","orcid":null},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Atsuki Munakata","raw_affiliation_strings":["Tokyo Institute of Technology, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology, Japan","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070734898","display_name":"Hiroki Nakahara","orcid":"https://orcid.org/0000-0002-5701-7466"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroki Nakahara","raw_affiliation_strings":["Tokyo Institute of Technology, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology, Japan","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101424734","display_name":"Shimpei Sato","orcid":"https://orcid.org/0000-0003-0292-1391"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shimpei Sato","raw_affiliation_strings":["Tokyo Institute of Technology, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology, Japan","institution_ids":["https://openalex.org/I114531698"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5069635518"],"corresponding_institution_ids":["https://openalex.org/I114531698"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06047537,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"85","last_page":"90"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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/T10320","display_name":"Neural Networks and Applications","score":0.9991999864578247,"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"}},{"id":"https://openalex.org/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9979000091552734,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7623006701469421},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7535881400108337},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.7131975889205933},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6961739659309387},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6457358002662659},{"id":"https://openalex.org/keywords/pointwise","display_name":"Pointwise","score":0.6203844547271729},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.48403066396713257},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47738975286483765},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.458459734916687},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3967837691307068},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3821644186973572},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.27098768949508667},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.18864679336547852},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11976268887519836}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7623006701469421},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7535881400108337},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.7131975889205933},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6961739659309387},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6457358002662659},{"id":"https://openalex.org/C2777984123","wikidata":"https://www.wikidata.org/wiki/Q9248237","display_name":"Pointwise","level":2,"score":0.6203844547271729},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.48403066396713257},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47738975286483765},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.458459734916687},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3967837691307068},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3821644186973572},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27098768949508667},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.18864679336547852},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11976268887519836},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ismvl.2019.00023","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ismvl.2019.00023","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 49th International Symposium on Multiple-Valued Logic (ISMVL)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W78356000","https://openalex.org/W639708223","https://openalex.org/W1665214252","https://openalex.org/W1832693441","https://openalex.org/W1903029394","https://openalex.org/W1923697677","https://openalex.org/W1990315422","https://openalex.org/W2097117768","https://openalex.org/W2119144962","https://openalex.org/W2135252045","https://openalex.org/W2170240176","https://openalex.org/W2307770531","https://openalex.org/W2519091744","https://openalex.org/W2546302380","https://openalex.org/W2559085405","https://openalex.org/W2565125333","https://openalex.org/W2570343428","https://openalex.org/W2585560244","https://openalex.org/W2591922920","https://openalex.org/W2613718673","https://openalex.org/W2613904329","https://openalex.org/W2625457103","https://openalex.org/W2762597430","https://openalex.org/W2766447205","https://openalex.org/W2949382160","https://openalex.org/W2963012544","https://openalex.org/W2963102117","https://openalex.org/W2963881378","https://openalex.org/W2964265128","https://openalex.org/W2964304707","https://openalex.org/W3102169921","https://openalex.org/W3106250896","https://openalex.org/W3118608800","https://openalex.org/W4240267682","https://openalex.org/W6603161775","https://openalex.org/W6620707391","https://openalex.org/W6637242042","https://openalex.org/W6640295612","https://openalex.org/W6685053522","https://openalex.org/W6697925102","https://openalex.org/W6730277886","https://openalex.org/W6734802215","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4361003569","https://openalex.org/W4225949190","https://openalex.org/W2981421796","https://openalex.org/W2964954556","https://openalex.org/W3034421924","https://openalex.org/W2982536526","https://openalex.org/W4386858688","https://openalex.org/W4380302312","https://openalex.org/W3008689640","https://openalex.org/W4385338604"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1],"networks":[2],"(CNNs)":[3],"are":[4,14],"primarily":[5],"a":[6,44,59,85,103,111,126,131],"cascaded":[7],"set":[8],"of":[9,26,50,78],"pattern":[10],"recognition":[11,82,161],"filters,":[12],"which":[13,48],"trained":[15],"by":[16,72],"big":[17],"data.":[18],"It":[19],"enables":[20],"us":[21],"to":[22,80],"solve":[23],"complex":[24],"problems":[25],"computer":[27],"vision":[28],"applications.":[29],"A":[30],"conventional":[31,51,104],"CNN":[32,46],"requires":[33],"numerous":[34],"parameters":[35],"(weights)":[36],"and":[37,58,110,163],"computations.":[38],"In":[39],"this":[40],"study,":[41],"we":[42,97,121],"propose":[43,122],"noise":[45,60,79,115,127,136],"(NCNN),":[47],"consists":[49],"convolutional":[52,61],"operation":[53,62,129],"in":[54,63,106,116,143],"the":[55,64,76,107,117,135,140,144,156],"former":[56,108],"layer":[57,109],"latter":[65,118],"layers.":[66],"Noise":[67],"convolution":[68,74,89,100,105,113,128],"can":[69,159],"be":[70],"realized":[71],"pointwise":[73],"with":[75,114,130],"addition":[77],"retain":[81],"accuracy":[83,162],"for":[84,125],"large":[86],"kernel":[87],"size":[88],"layer.":[90],"Using":[91],"data":[92],"obtained":[93],"from":[94],"theoretical":[95],"analysis,":[96],"apply":[98],"various":[99],"layers":[101],"including":[102],"point-wise":[112],"one.":[119],"Further,":[120],"an":[123],"architecture":[124],"pseudo-random":[132],"circuit":[133],"as":[134],"generator.":[137],"We":[138],"implement":[139],"proposed":[141,157],"NCNN":[142,158],"Xilinx":[145],"Inc.":[146],"ZCU104":[147],"FPGA":[148],"evaluation":[149],"board.":[150],"The":[151],"experimental":[152],"results":[153],"show":[154],"that":[155],"preserve":[160],"achieve":[164],"high":[165],"performance.":[166]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
