{"id":"https://openalex.org/W2972432270","doi":"https://doi.org/10.1145/3354265.3354274","title":"Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout","display_name":"Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout","publication_year":2019,"publication_date":"2019-07-23","ids":{"openalex":"https://openalex.org/W2972432270","doi":"https://doi.org/10.1145/3354265.3354274","mag":"2972432270"},"language":"en","primary_location":{"id":"doi:10.1145/3354265.3354274","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3354265.3354274","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Neuromorphic Systems","raw_type":"proceedings-article"},"type":"preprint","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/A5021501780","display_name":"Nathan Wycoff","orcid":"https://orcid.org/0000-0002-0336-7401"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]},{"id":"https://openalex.org/I1282105669","display_name":"Argonne National Laboratory","ror":"https://ror.org/05gvnxz63","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282105669","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nathan Wycoff","raw_affiliation_strings":["Virginia Tech, Blacksburg, Virginia and Mathematics and Computer Science Division of Argonne National Laboratory"],"affiliations":[{"raw_affiliation_string":"Virginia Tech, Blacksburg, Virginia and Mathematics and Computer Science Division of Argonne National Laboratory","institution_ids":["https://openalex.org/I859038795","https://openalex.org/I1282105669"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068721920","display_name":"Prasanna Balaprakash","orcid":"https://orcid.org/0000-0002-0292-5715"},"institutions":[{"id":"https://openalex.org/I1282105669","display_name":"Argonne National Laboratory","ror":"https://ror.org/05gvnxz63","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282105669","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Prasanna Balaprakash","raw_affiliation_strings":["Argonne National Laboratory, Lemont, Illinois"],"affiliations":[{"raw_affiliation_string":"Argonne National Laboratory, Lemont, Illinois","institution_ids":["https://openalex.org/I1282105669"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049471852","display_name":"Fangfang Xia","orcid":"https://orcid.org/0000-0001-6567-0564"},"institutions":[{"id":"https://openalex.org/I1282105669","display_name":"Argonne National Laboratory","ror":"https://ror.org/05gvnxz63","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282105669","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fangfang Xia","raw_affiliation_strings":["Argonne National Laboratory, Lemont, Illinois"],"affiliations":[{"raw_affiliation_string":"Argonne National Laboratory, Lemont, Illinois","institution_ids":["https://openalex.org/I1282105669"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5021501780"],"corresponding_institution_ids":["https://openalex.org/I859038795","https://openalex.org/I1282105669"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08579665,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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/T10581","display_name":"Neural dynamics and brain function","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"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/T11948","display_name":"Machine Learning in Materials Science","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7312516570091248},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.711482584476471},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.7054464817047119},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7028428316116333},{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.6923909783363342},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.6619373559951782},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.606223464012146},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5264055132865906},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5044759511947632},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3831290900707245}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7312516570091248},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.711482584476471},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.7054464817047119},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7028428316116333},{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.6923909783363342},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.6619373559951782},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.606223464012146},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5264055132865906},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5044759511947632},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3831290900707245}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3354265.3354274","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3354265.3354274","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Neuromorphic Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6800000071525574,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W164706946","https://openalex.org/W1645800954","https://openalex.org/W2006370340","https://openalex.org/W2020676607","https://openalex.org/W2065125569","https://openalex.org/W2233731247","https://openalex.org/W2293040502","https://openalex.org/W2557283755","https://openalex.org/W2608863179","https://openalex.org/W2609532991","https://openalex.org/W2775079417","https://openalex.org/W2783525259","https://openalex.org/W2902518737","https://openalex.org/W2912999619","https://openalex.org/W3004557030","https://openalex.org/W3196769885","https://openalex.org/W4211049957","https://openalex.org/W4212774754","https://openalex.org/W4238614602","https://openalex.org/W4240159778"],"related_works":["https://openalex.org/W3089892344","https://openalex.org/W2960220682","https://openalex.org/W4313442939","https://openalex.org/W4386227293","https://openalex.org/W4372267706","https://openalex.org/W2885510266","https://openalex.org/W4288055417","https://openalex.org/W4205804651","https://openalex.org/W4287758233","https://openalex.org/W3136467750"],"abstract_inverted_index":{"As":[0],"neural":[1,43,75,94,118,148],"networks":[2,76,95],"have":[3],"begun":[4],"performing":[5],"increasingly":[6],"critical":[7],"tasks":[8],"for":[9,18,42,60,130,156],"society":[10],"ranging":[11],"from":[12,77,176],"driving":[13],"cars":[14],"to":[15,26,81,96,104],"identifying":[16],"candidates":[17],"drug":[19,153],"development,":[20],"the":[21,50,54,63,68,78,82,106,110,121,141,145,167,180],"value":[22],"of":[23,53,62,73,91,108],"their":[24,32,97],"ability":[25],"perform":[27],"uncertainty":[28],"quantification":[29],"(UQ)":[30],"in":[31],"predictions":[33,59],"has":[34,87],"risen":[35],"commensurately.":[36],"Permanent":[37],"dropout,":[38],"a":[39,151,171],"popular":[40],"method":[41],"network":[44],"UQ,":[45],"involves":[46],"injecting":[47],"stochasticity":[48],"into":[49],"inference":[51,83,111],"phase":[52,80,112],"model":[55],"and":[56,70,124],"creating":[57],"many":[58],"each":[61],"test":[64],"data.":[65],"This":[66],"shifts":[67],"computational":[69,123],"energy":[71,125],"burden":[72],"deep":[74,93],"training":[79],"phase.":[84],"Recent":[85],"work":[86],"demonstrated":[88],"near-lossless":[89],"conversion":[90],"classical":[92,181],"spiking":[98,117,147,168],"counterparts.":[99],"We":[100,139],"use":[101,132],"these":[102],"results":[103,164],"demonstrate":[105,140],"feasibility":[107],"conducting":[109],"with":[113],"permanent":[114],"dropout":[115],"on":[116,136,150],"networks,":[119],"mitigating":[120],"technique's":[122],"burden,":[126],"which":[127],"is":[128,161],"essential":[129],"its":[131],"at":[133],"scale":[134],"or":[135],"edge":[137],"platforms.":[138],"proposed":[142],"approach":[143],"via":[144],"Nengo":[146],"simulator":[149],"combination":[152],"therapy":[154],"dataset":[155],"cancer":[157],"treatment,":[158],"where":[159],"UQ":[160],"critical.":[162],"Our":[163],"indicate":[165],"that":[166,177],"approximation":[169],"gives":[170],"predictive":[172],"distribution":[173],"practically":[174],"indistinguishable":[175],"given":[178],"by":[179],"network.":[182]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}