{"id":"https://openalex.org/W2914725495","doi":"https://doi.org/10.1109/reconfig.2018.8641716","title":"Throughput-Optimized Frequency Domain CNN with Fixed-Point Quantization on FPGA","display_name":"Throughput-Optimized Frequency Domain CNN with Fixed-Point Quantization on FPGA","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2914725495","doi":"https://doi.org/10.1109/reconfig.2018.8641716","mag":"2914725495"},"language":"en","primary_location":{"id":"doi:10.1109/reconfig.2018.8641716","is_oa":false,"landing_page_url":"https://doi.org/10.1109/reconfig.2018.8641716","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Conference on ReConFigurable Computing and FPGAs (ReConFig)","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/A5101979303","display_name":"Weiyi Sun","orcid":"https://orcid.org/0000-0002-0432-6636"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weiyi Sun","raw_affiliation_strings":["Department of Microelectronics and Nanoelectronics, Tsinghua University"],"affiliations":[{"raw_affiliation_string":"Department of Microelectronics and Nanoelectronics, Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014103790","display_name":"Hanqing Zeng","orcid":"https://orcid.org/0000-0002-2578-2147"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hanqing Zeng","raw_affiliation_strings":["Ming Hsieh Departmnet of Electrical Engineering, University of Southern California"],"affiliations":[{"raw_affiliation_string":"Ming Hsieh Departmnet of Electrical Engineering, University of Southern California","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045357286","display_name":"Yi-Hua E. Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210095624","display_name":"Alibaba Group (United States)","ror":"https://ror.org/00rn0m335","country_code":"US","type":"company","lineage":["https://openalex.org/I4210095624","https://openalex.org/I45928872"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yi-hua Edward Yang","raw_affiliation_strings":["Machine Intelligence Technology, Alibaba Group"],"affiliations":[{"raw_affiliation_string":"Machine Intelligence Technology, Alibaba Group","institution_ids":["https://openalex.org/I4210095624"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033166029","display_name":"Viktor K. Prasanna","orcid":"https://orcid.org/0000-0002-1609-8589"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Viktor Prasanna","raw_affiliation_strings":["Ming Hsieh Departmnet of Electrical Engineering, University of Southern California"],"affiliations":[{"raw_affiliation_string":"Ming Hsieh Departmnet of Electrical Engineering, University of Southern California","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101979303"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.4178,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.68747489,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9918000102043152,"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/computer-science","display_name":"Computer science","score":0.7975366115570068},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.7575973868370056},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.7105445861816406},{"id":"https://openalex.org/keywords/reconfigurability","display_name":"Reconfigurability","score":0.6399263739585876},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.5056324005126953},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.4321989119052887},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4301531910896301},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.42477428913116455},{"id":"https://openalex.org/keywords/fast-fourier-transform","display_name":"Fast Fourier transform","score":0.42246416211128235},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.41703033447265625},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3956086039543152},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.35411325097084045},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.2916790544986725},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.16673925518989563}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7975366115570068},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.7575973868370056},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.7105445861816406},{"id":"https://openalex.org/C2780149590","wikidata":"https://www.wikidata.org/wiki/Q7302742","display_name":"Reconfigurability","level":2,"score":0.6399263739585876},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.5056324005126953},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.4321989119052887},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4301531910896301},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.42477428913116455},{"id":"https://openalex.org/C75172450","wikidata":"https://www.wikidata.org/wiki/Q623950","display_name":"Fast Fourier transform","level":2,"score":0.42246416211128235},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.41703033447265625},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3956086039543152},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.35411325097084045},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.2916790544986725},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.16673925518989563},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/reconfig.2018.8641716","is_oa":false,"landing_page_url":"https://doi.org/10.1109/reconfig.2018.8641716","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Conference on ReConFigurable Computing and FPGAs (ReConFig)","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":23,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1903029394","https://openalex.org/W1969057818","https://openalex.org/W2098290609","https://openalex.org/W2163491234","https://openalex.org/W2163605009","https://openalex.org/W2171656599","https://openalex.org/W2233116163","https://openalex.org/W2286365479","https://openalex.org/W2523838129","https://openalex.org/W2584616277","https://openalex.org/W2786216825","https://openalex.org/W2786845740","https://openalex.org/W2792503273","https://openalex.org/W2962835968","https://openalex.org/W2963340555","https://openalex.org/W2970455671","https://openalex.org/W6637373629","https://openalex.org/W6640054144","https://openalex.org/W6640442106","https://openalex.org/W6684191040","https://openalex.org/W6696004547","https://openalex.org/W6727533809"],"related_works":["https://openalex.org/W1544665014","https://openalex.org/W2748364266","https://openalex.org/W4243861219","https://openalex.org/W2556374054","https://openalex.org/W2069545207","https://openalex.org/W3204400881","https://openalex.org/W3214410901","https://openalex.org/W3204296682","https://openalex.org/W3183118997","https://openalex.org/W2917767146"],"abstract_inverted_index":{"State-of-the-art":[0],"hardware":[1],"accelerators":[2],"for":[3,98,113,133],"large":[4],"scale":[5],"CNNs":[6],"face":[7],"two":[8],"challenges:":[9,34],"high":[10,16,54],"computation":[11],"complexity":[12],"of":[13,65,72,80,85,88,116,131],"convolution,":[14],"and":[15,38,94,123,149],"on-chip":[17,147],"memory":[18,148],"consumption":[19],"by":[20,164],"weight":[21],"kernels.":[22],"Two":[23],"techniques":[24],"have":[25],"been":[26],"proposed":[27,126],"in":[28,173],"the":[29,63,70,78,86,99,106,129,160],"literature":[30],"to":[31,52,166],"address":[32],"these":[33],"frequency":[35,48,74],"domain":[36,40,49,75],"convolution":[37],"space":[39],"fixed-point":[41],"quantization.":[42],"In":[43],"this":[44],"paper,":[45],"we":[46,90,109],"propose":[47,110],"quantization":[50,66,111],"schemes":[51,112],"achieve":[53],"throughput":[55,122,163],"CNN":[56,161],"inference":[57,162],"on":[58,69,105,153],"FPGAs.":[59],"We":[60],"first":[61],"analyze":[62],"impact":[64],"bit":[67],"width":[68],"accuracy":[71],"a":[73,92,95],"CNN,":[76],"via":[77],"metric":[79],"Signal-to-Quantization-Noise-Ratio":[81],"(SQNR).":[82],"Taking":[83],"advantage":[84],"reconfigurability":[87],"FPGAs,":[89],"design":[91,139],"statically-reconfigurable":[93],"dynamically-reconfigurable":[96],"architecture":[97],"quantized":[100],"convolutional":[101,135],"layers.":[102],"Then,":[103],"based":[104],"SQNR":[107],"analysis,":[108],"both":[114],"types":[115],"architectures,":[117],"achieving":[118],"optimal":[119],"tradeoff":[120],"between":[121],"accuracy.":[124,174],"The":[125],"quantizer":[127],"allocates":[128],"number":[130],"bits":[132],"each":[134],"layer":[136],"under":[137],"various":[138],"constraints,":[140],"including":[141],"overall":[142],"SQNR,":[143],"available":[144],"DSP":[145],"resources,":[146],"off-chip":[150],"bandwidth.":[151],"Experiments":[152],"AlexNet":[154],"show":[155],"that":[156],"our":[157],"designs":[158],"improve":[159],"1.45$\\times$":[165],"8.44$\\times$,":[167],"with":[168],"negligible":[169],"(<":[170],"0.5%)":[171],"loss":[172]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}