{"id":"https://openalex.org/W3035963492","doi":"https://doi.org/10.23919/date48585.2020.9116302","title":"BYNQNet: Bayesian Neural Network with Quadratic Activations for Sampling-Free Uncertainty Estimation on FPGA","display_name":"BYNQNet: Bayesian Neural Network with Quadratic Activations for Sampling-Free Uncertainty Estimation on FPGA","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3035963492","doi":"https://doi.org/10.23919/date48585.2020.9116302","mag":"3035963492"},"language":"en","primary_location":{"id":"doi:10.23919/date48585.2020.9116302","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date48585.2020.9116302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE)","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/A5001875763","display_name":"Hiromitsu Awano","orcid":"https://orcid.org/0000-0001-9288-471X"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiromitsu Awano","raw_affiliation_strings":["Osaka University,Graduate School of Information Science and Technology,Osaka,Japan,565-0871"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Osaka University,Graduate School of Information Science and Technology,Osaka,Japan,565-0871","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002405139","display_name":"Masanori Hashimoto","orcid":"https://orcid.org/0000-0002-0377-2108"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"The University of Osaka","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masanori Hashimoto","raw_affiliation_strings":["Osaka University,Graduate School of Information Science and Technology,Osaka,Japan,565-0871"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Osaka University,Graduate School of Information Science and Technology,Osaka,Japan,565-0871","institution_ids":["https://openalex.org/I98285908"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.7082,"has_fulltext":false,"cited_by_count":40,"citation_normalized_percentile":{"value":0.92114268,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1402","last_page":"1407"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9990000128746033,"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"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9990000128746033,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T12072","display_name":"Machine Learning and Algorithms","score":0.9972000122070312,"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/artificial-neural-network","display_name":"Artificial neural network","score":0.5867161750793457},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5266016721725464},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"Statistical inference","score":0.5027871131896973},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.48733171820640564},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4815009832382202},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4460000693798065},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3460160493850708},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3200884461402893},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2885330617427826},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17505648732185364},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.13272187113761902}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5867161750793457},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5266016721725464},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.5027871131896973},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.48733171820640564},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4815009832382202},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4460000693798065},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3460160493850708},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3200884461402893},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2885330617427826},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17505648732185364},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.13272187113761902},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/date48585.2020.9116302","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date48585.2020.9116302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8999999761581421,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W582134693","https://openalex.org/W601603264","https://openalex.org/W1959608418","https://openalex.org/W2097117768","https://openalex.org/W2111051539","https://openalex.org/W2127538960","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2257979135","https://openalex.org/W2342840547","https://openalex.org/W2435473771","https://openalex.org/W2600383743","https://openalex.org/W2740067745","https://openalex.org/W2776940252","https://openalex.org/W2785369415","https://openalex.org/W2785534585","https://openalex.org/W2884490794","https://openalex.org/W2897001865","https://openalex.org/W2951983780","https://openalex.org/W2962894046","https://openalex.org/W2964059111","https://openalex.org/W2965798711","https://openalex.org/W3101788429","https://openalex.org/W4294604590","https://openalex.org/W6617145748","https://openalex.org/W6618476874","https://openalex.org/W6640963894","https://openalex.org/W6679143118","https://openalex.org/W6684191040","https://openalex.org/W6704559304","https://openalex.org/W6717974185","https://openalex.org/W6735443497","https://openalex.org/W6747157835","https://openalex.org/W6755843720"],"related_works":["https://openalex.org/W2055243143","https://openalex.org/W4321636575","https://openalex.org/W3000984192","https://openalex.org/W137830373","https://openalex.org/W4286952477","https://openalex.org/W2134368299","https://openalex.org/W4321348134","https://openalex.org/W2103073163","https://openalex.org/W4387929287","https://openalex.org/W3017020030"],"abstract_inverted_index":{"An":[0],"efficient":[1],"inference":[2,36,62,89],"algorithm":[3],"for":[4,100,120,138],"Bayesian":[5,11],"neural":[6,12,24,51],"network":[7,13,35,52,55,81],"(BNN)":[8],"named":[9],"BYNQNet,":[10],"with":[14,48,78,172],"quadratic":[15,147],"activations,":[16],"and":[17,130,151,201,217],"its":[18],"FPGA":[19,188],"implementation":[20],"are":[21],"proposed.":[22],"As":[23],"networks":[25],"find":[26],"applications":[27],"in":[28,34,50,63,85],"mission":[29],"critical":[30],"systems,":[31],"uncertainty":[32,49,154],"estimations":[33],"become":[37],"increasingly":[38],"important.":[39],"BNN":[40,64,101,174,224],"is":[41,74],"a":[42,71,112],"theoretically":[43],"grounded":[44],"solution":[45],"to":[46,91,142,215],"deal":[47],"by":[53],"treating":[54],"parameters":[56],"as":[57,111,128],"random":[58],"variables.":[59],"However,":[60],"an":[61],"involves":[65],"Monte":[66],"Carlo":[67],"(MC)":[68],"sampling,":[69],"i.e.,":[70],"stochastic":[72],"forwarding":[73],"repeated":[75],"N":[76,86],"times":[77,87],"randomly":[79],"sampled":[80],"parameters,":[82],"which":[83,132,175,213],"results":[84],"slower":[88],"compared":[90],"non-Bayesian":[92],"approach.":[93],"Although":[94],"recent":[95],"papers":[96],"proposed":[97],"sampling-free":[98],"algorithms":[99],"inference,":[102],"they":[103],"still":[104],"require":[105],"evaluation":[106],"of":[107,117,136,193,205],"complex":[108],"functions":[109,126,150],"such":[110,127],"cumulative":[113],"distribution":[114,119],"function":[115],"(CDF)":[116],"Gaussian":[118],"propagating":[121],"uncertainties":[122],"through":[123],"nonlinear":[124,148],"activation":[125,149],"ReLU":[129],"Heaviside,":[131],"requires":[133,176],"considerable":[134],"amount":[135],"resources":[137],"hardware":[139],"implementation.":[140],"Contrary":[141],"conventional":[143],"BNN,":[144],"BYNQNet":[145,168,183],"employs":[146],"hence":[152],"the":[153,191,202,221],"propagation":[155],"can":[156],"be":[157],"achieved":[158],"using":[159],"only":[160],"polynomial":[161],"operations.":[162],"Our":[163],"numerical":[164],"experiment":[165],"reveals":[166],"that":[167,182],"has":[169],"comparative":[170],"accuracy":[171],"MC-based":[173,223],"N=10":[177],"forwardings.":[178],"We":[179],"also":[180],"demonstrate":[181],"implemented":[184],"on":[185],"Xilinx":[186],"PYNQ-Z1":[187],"board":[189],"achieves":[190],"throughput":[192],"131\u00d710":[194],"<sup":[195,207],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[196,208],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">3</sup>":[197,209],"images":[198,210],"per":[199,211],"second":[200],"energy":[203],"efficiency":[204],"44.7\u00d710":[206],"joule,":[212],"corresponds":[214],"4.07\u00d7":[216],"8.99\u00d7":[218],"improvements":[219],"from":[220],"state-of-the-art":[222],"accelerator.":[225]},"counts_by_year":[{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":5}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}