{"id":"https://openalex.org/W2964010167","doi":"https://doi.org/10.1145/3194554.3194597","title":"Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs","display_name":"Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs","publication_year":2018,"publication_date":"2018-05-30","ids":{"openalex":"https://openalex.org/W2964010167","doi":"https://doi.org/10.1145/3194554.3194597","mag":"2964010167"},"language":"en","primary_location":{"id":"doi:10.1145/3194554.3194597","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3194554.3194597","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 Great Lakes Symposium on VLSI","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/A5040614886","display_name":"Chuanhao Zhuge","orcid":null},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chuanhao Zhuge","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082291284","display_name":"Xinheng Liu","orcid":"https://orcid.org/0000-0003-4785-1411"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xinheng Liu","raw_affiliation_strings":["University of Illinois at Urbana-Champaign &amp; Inspirit IoT, Inc., Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign &amp; Inspirit IoT, Inc., Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100330729","display_name":"Xiaofan Zhang","orcid":"https://orcid.org/0000-0001-5081-3972"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaofan Zhang","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012589555","display_name":"Sudeep Gummadi","orcid":null},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sudeep Gummadi","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030156276","display_name":"Jinjun Xiong","orcid":"https://orcid.org/0000-0002-2620-4859"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jinjun Xiong","raw_affiliation_strings":["IBM T. J. Watson Research Center, Yorktown Height, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center, Yorktown Height, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056321228","display_name":"Deming Chen","orcid":"https://orcid.org/0000-0002-3016-0270"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Deming Chen","raw_affiliation_strings":["University of Illinois at Urbana-Champaign &amp; Inspirit IoT Inc., Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign &amp; Inspirit IoT Inc., Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5040614886"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":3.3426,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.94621398,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"123","last_page":"128"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9995999932289124,"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/T11448","display_name":"Face recognition and analysis","score":0.9995999932289124,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9994000196456909,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9990000128746033,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8530677556991577},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.7861527800559998},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.7161348462104797},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7127742767333984},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.6718037128448486},{"id":"https://openalex.org/keywords/fast-fourier-transform","display_name":"Fast Fourier transform","score":0.6380440592765808},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.6100687384605408},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.5930846333503723},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47186821699142456},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.46305370330810547},{"id":"https://openalex.org/keywords/low-latency","display_name":"Low latency (capital markets)","score":0.4592149257659912},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.405880331993103},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2906840443611145},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.24133187532424927}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8530677556991577},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.7861527800559998},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7161348462104797},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7127742767333984},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.6718037128448486},{"id":"https://openalex.org/C75172450","wikidata":"https://www.wikidata.org/wiki/Q623950","display_name":"Fast Fourier transform","level":2,"score":0.6380440592765808},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.6100687384605408},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.5930846333503723},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47186821699142456},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46305370330810547},{"id":"https://openalex.org/C46637626","wikidata":"https://www.wikidata.org/wiki/Q6693015","display_name":"Low latency (capital markets)","level":2,"score":0.4592149257659912},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.405880331993103},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2906840443611145},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.24133187532424927},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3194554.3194597","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3194554.3194597","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 Great Lakes Symposium on VLSI","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7900000214576721,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320316786","display_name":"Center for Cognitive Computing Systems Research","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1789336918","https://openalex.org/W1947481528","https://openalex.org/W1982052956","https://openalex.org/W2094756095","https://openalex.org/W2096733369","https://openalex.org/W2097117768","https://openalex.org/W2146851106","https://openalex.org/W2159584133","https://openalex.org/W2172654076","https://openalex.org/W2337344472","https://openalex.org/W2524802307","https://openalex.org/W2528015417","https://openalex.org/W2574797063","https://openalex.org/W2584616277","https://openalex.org/W2762910930","https://openalex.org/W2773042115","https://openalex.org/W2951460453","https://openalex.org/W2964209782","https://openalex.org/W2964299589","https://openalex.org/W3099206234"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W3157543420","https://openalex.org/W3205411230","https://openalex.org/W4286899009","https://openalex.org/W9168048","https://openalex.org/W4300849822","https://openalex.org/W4376480820","https://openalex.org/W3155891479","https://openalex.org/W3029351463","https://openalex.org/W4308600690"],"abstract_inverted_index":{"Deep":[0],"Convolutional":[1],"Neural":[2],"Networks":[3],"(CNN)":[4],"have":[5],"become":[6,39],"a":[7,40,111,129,140],"Swiss":[8],"knife":[9],"in":[10,107],"solving":[11],"critical":[12],"arti":[13],"cial":[14],"intelligence":[15],"tasks.":[16],"However,":[17],"deploying":[18],"deep":[19,45],"CNN":[20,101],"models":[21],"for":[22],"latency-critical":[23],"tasks":[24],"remains":[25],"to":[26,43,48,80,96,139],"be":[27],"challenging":[28],"because":[29],"of":[30,34,88],"the":[31],"complex":[32],"nature":[33],"CNNs.":[35],"Recently,":[36],"FPGA":[37,147],"has":[38],"favorable":[41],"device":[42,132],"accelerate":[44],"CNNs":[46],"thanks":[47],"its":[49],"high":[50],"parallel":[51],"processing":[52],"capability":[53],"and":[54,70,75,144],"energy":[55],"e":[56],"ciency.":[57],"In":[58],"this":[59],"work,":[60],"we":[61],"explore":[62],"di":[63,85],"erent":[64,86],"fast":[65],"convolution":[66],"algorithms":[67],"including":[68],"Winograd":[69],"Fast":[71],"Fourier":[72],"Transform":[73],"(FFT),":[74],"nd":[76],"an":[77,93],"optimal":[78],"strategy":[79],"apply":[81],"them":[82],"together":[83],"on":[84,99,121,128],"types":[87],"convolutions.":[89],"We":[90,109],"also":[91],"propose":[92],"optimization":[94],"scheme":[95],"exploit":[97],"parallelism":[98],"novel":[100],"architectures":[102],"such":[103],"as":[104],"Inception":[105],"modules":[106],"GoogLeNet.":[108],"implement":[110],"con":[112],"gurable":[113],"IP-based":[114],"face":[115],"recognition":[116],"acceler-":[117],"ation":[118],"system":[119],"based":[120],"FaceNet":[122],"using":[123],"High-Level":[124],"Synthesis.":[125],"Our":[126],"implementation":[127],"Xilinx":[130],"Ultrascale":[131],"achieves":[133],"3.75x":[134],"la-":[135],"tency":[136],"speedup":[137],"compared":[138],"high-end":[141],"NVIDIA":[142],"GPU":[143],"surpasses":[145],"previous":[146],"results":[148],"signi":[149],"cantly.":[150]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":13},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
