{"id":"https://openalex.org/W2950723943","doi":"https://doi.org/10.1109/vlsi-dat.2019.8741633","title":"Energy Efficient CNN Inference Accelerator Using Fast Fourier Transform","display_name":"Energy Efficient CNN Inference Accelerator Using Fast Fourier Transform","publication_year":2019,"publication_date":"2019-04-01","ids":{"openalex":"https://openalex.org/W2950723943","doi":"https://doi.org/10.1109/vlsi-dat.2019.8741633","mag":"2950723943"},"language":"en","primary_location":{"id":"doi:10.1109/vlsi-dat.2019.8741633","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vlsi-dat.2019.8741633","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Symposium on VLSI Design, Automation and Test (VLSI-DAT)","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/A5033494670","display_name":"Ya-Chin Chung","orcid":null},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Ya-Chin Chung","raw_affiliation_strings":["Institute of Electronics, National Chiao-Tung University, Hsinchu, Taiwan"],"affiliations":[{"raw_affiliation_string":"Institute of Electronics, National Chiao-Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112433869","display_name":"Po-Hsiang Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Po-Hsiang Cheng","raw_affiliation_strings":["Institute of Electronics, National Chiao-Tung University, Hsinchu, Taiwan"],"affiliations":[{"raw_affiliation_string":"Institute of Electronics, National Chiao-Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100642147","display_name":"Youfu Li","orcid":"https://orcid.org/0000-0002-5227-1326"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chih-Wei Liu","raw_affiliation_strings":["Institute of Electronics, National Chiao-Tung University, Hsinchu, Taiwan"],"affiliations":[{"raw_affiliation_string":"Institute of Electronics, National Chiao-Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5033494670"],"corresponding_institution_ids":["https://openalex.org/I148366613"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.04432312,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9998999834060669,"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":0.9998999834060669,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9977999925613403,"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/T10320","display_name":"Neural Networks and Applications","score":0.9958000183105469,"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/fast-fourier-transform","display_name":"Fast Fourier transform","score":0.8461569547653198},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7161529064178467},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.6932367086410522},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6474384665489197},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.559898316860199},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5040701627731323},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.49849414825439453},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4901328980922699},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4820035994052887},{"id":"https://openalex.org/keywords/cmos","display_name":"CMOS","score":0.4413950443267822},{"id":"https://openalex.org/keywords/cooley\u2013tukey-fft-algorithm","display_name":"Cooley\u2013Tukey FFT algorithm","score":0.4167129695415497},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.2969866991043091},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23434042930603027},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16288435459136963},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1305093765258789},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09140944480895996},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.07876697182655334},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.07768696546554565}],"concepts":[{"id":"https://openalex.org/C75172450","wikidata":"https://www.wikidata.org/wiki/Q623950","display_name":"Fast Fourier transform","level":2,"score":0.8461569547653198},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7161529064178467},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.6932367086410522},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6474384665489197},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.559898316860199},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5040701627731323},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.49849414825439453},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4901328980922699},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4820035994052887},{"id":"https://openalex.org/C46362747","wikidata":"https://www.wikidata.org/wiki/Q173431","display_name":"CMOS","level":2,"score":0.4413950443267822},{"id":"https://openalex.org/C1370844","wikidata":"https://www.wikidata.org/wiki/Q5167446","display_name":"Cooley\u2013Tukey FFT algorithm","level":3,"score":0.4167129695415497},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.2969866991043091},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23434042930603027},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16288435459136963},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1305093765258789},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09140944480895996},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.07876697182655334},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.07768696546554565},{"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/vlsi-dat.2019.8741633","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vlsi-dat.2019.8741633","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Symposium on VLSI Design, Automation and Test (VLSI-DAT)","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":0.9100000262260437}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2097117768","https://openalex.org/W2106445473","https://openalex.org/W2163605009","https://openalex.org/W2255738257","https://openalex.org/W2560084232","https://openalex.org/W2952682304","https://openalex.org/W2962835968","https://openalex.org/W2963340555","https://openalex.org/W2963751813","https://openalex.org/W3024621361","https://openalex.org/W6637373629","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W2030727626","https://openalex.org/W2360756276","https://openalex.org/W2042527476","https://openalex.org/W2066431770","https://openalex.org/W2006537647","https://openalex.org/W2371538653","https://openalex.org/W4246835537","https://openalex.org/W2011193550","https://openalex.org/W3046243388","https://openalex.org/W2010918451"],"abstract_inverted_index":{"We":[0],"use":[1],"FFT-based":[2,85],"convolution":[3],"in":[4,11,30,50,58],"frequency":[5,51],"domain":[6,52],"to":[7,22,39],"reduce":[8,24],"computational":[9,35],"complexity":[10],"CNNs.":[12],"The":[13,42],"properties":[14],"of":[15],"conjugate":[16],"symmetry":[17],"and":[18,76],"down-sampling":[19],"is":[20,53,88],"adopted":[21],"further":[23],"complexity.":[25],"By":[26],"eliminating":[27,47],"filter":[28,48],"weights":[29,49],"CNNs":[31],"that":[32,46,57],"can":[33],"save":[34],"requirement":[36],"but":[37],"lead":[38],"accuracy":[40],"loss.":[41],"simulation":[43],"result":[44],"reveals":[45],"more":[54],"accurate":[55],"than":[56],"time":[59],"domain.":[60],"With":[61],"the":[62,72],"proposed":[63],"design":[64],"synthesized":[65],"by":[66],"TSMC":[67],"90":[68],"nm":[69],"CMOS":[70],"technology,":[71],"total":[73],"latency,":[74],"power":[75],"energy":[77],"are":[78],"considerably":[79],"competitive.":[80],"As":[81],"a":[82],"result,":[83],"our":[84],"CNN":[86],"accelerator":[87],"energy-efficient.":[89]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}