{"id":"https://openalex.org/W3048404194","doi":"https://doi.org/10.1109/vlsicircuits18222.2020.9162784","title":"A 3mm<sup>2</sup> Programmable Bayesian Inference Accelerator for Unsupervised Machine Perception using Parallel Gibbs Sampling in 16nm","display_name":"A 3mm<sup>2</sup> Programmable Bayesian Inference Accelerator for Unsupervised Machine Perception using Parallel Gibbs Sampling in 16nm","publication_year":2020,"publication_date":"2020-06-01","ids":{"openalex":"https://openalex.org/W3048404194","doi":"https://doi.org/10.1109/vlsicircuits18222.2020.9162784","mag":"3048404194"},"language":"en","primary_location":{"id":"doi:10.1109/vlsicircuits18222.2020.9162784","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vlsicircuits18222.2020.9162784","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Symposium on VLSI Circuits","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/A5074229216","display_name":"Glenn G. Ko","orcid":null},"institutions":[{"id":"https://openalex.org/I2801851002","display_name":"Harvard University Press","ror":"https://ror.org/006v7bf86","country_code":"US","type":"other","lineage":["https://openalex.org/I136199984","https://openalex.org/I2801851002"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Glenn G. Ko","raw_affiliation_strings":["Harvard University, MA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harvard University, MA","institution_ids":["https://openalex.org/I2801851002"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030969018","display_name":"Yuji Chai","orcid":null},"institutions":[{"id":"https://openalex.org/I2801851002","display_name":"Harvard University Press","ror":"https://ror.org/006v7bf86","country_code":"US","type":"other","lineage":["https://openalex.org/I136199984","https://openalex.org/I2801851002"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuji Chai","raw_affiliation_strings":["Harvard University, MA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harvard University, MA","institution_ids":["https://openalex.org/I2801851002"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102005543","display_name":"Marco Donato","orcid":"https://orcid.org/0000-0002-9354-3447"},"institutions":[{"id":"https://openalex.org/I2801851002","display_name":"Harvard University Press","ror":"https://ror.org/006v7bf86","country_code":"US","type":"other","lineage":["https://openalex.org/I136199984","https://openalex.org/I2801851002"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marco Donato","raw_affiliation_strings":["Harvard University, MA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harvard University, MA","institution_ids":["https://openalex.org/I2801851002"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088853105","display_name":"Paul N. Whatmough","orcid":"https://orcid.org/0000-0002-1865-6492"},"institutions":[{"id":"https://openalex.org/I2801851002","display_name":"Harvard University Press","ror":"https://ror.org/006v7bf86","country_code":"US","type":"other","lineage":["https://openalex.org/I136199984","https://openalex.org/I2801851002"]},{"id":"https://openalex.org/I4210156213","display_name":"American Rock Mechanics Association","ror":"https://ror.org/05vfrxy92","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210156213"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Paul N. Whatmough","raw_affiliation_strings":["Arm Research, MA","Harvard University, MA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arm Research, MA","institution_ids":["https://openalex.org/I4210156213"]},{"raw_affiliation_string":"Harvard University, MA","institution_ids":["https://openalex.org/I2801851002"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005762501","display_name":"Thierry Tambe","orcid":"https://orcid.org/0000-0002-6411-9620"},"institutions":[{"id":"https://openalex.org/I2801851002","display_name":"Harvard University Press","ror":"https://ror.org/006v7bf86","country_code":"US","type":"other","lineage":["https://openalex.org/I136199984","https://openalex.org/I2801851002"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Thierry Tambe","raw_affiliation_strings":["Harvard University, MA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harvard University, MA","institution_ids":["https://openalex.org/I2801851002"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008408555","display_name":"Rob A. Rutenbar","orcid":null},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rob A. Rutenbar","raw_affiliation_strings":["University of Pittsburgh, PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pittsburgh, PA","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026496503","display_name":"David Brooks","orcid":"https://orcid.org/0000-0002-0662-7889"},"institutions":[{"id":"https://openalex.org/I2801851002","display_name":"Harvard University Press","ror":"https://ror.org/006v7bf86","country_code":"US","type":"other","lineage":["https://openalex.org/I136199984","https://openalex.org/I2801851002"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Brooks","raw_affiliation_strings":["Harvard University, MA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harvard University, MA","institution_ids":["https://openalex.org/I2801851002"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043327132","display_name":"Gu-Yeon Wei","orcid":"https://orcid.org/0000-0001-5730-9904"},"institutions":[{"id":"https://openalex.org/I2801851002","display_name":"Harvard University Press","ror":"https://ror.org/006v7bf86","country_code":"US","type":"other","lineage":["https://openalex.org/I136199984","https://openalex.org/I2801851002"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gu-Yeon Wei","raw_affiliation_strings":["Harvard University, MA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harvard University, MA","institution_ids":["https://openalex.org/I2801851002"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0768,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.7991395,"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":"2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9940000176429749,"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":0.9940000176429749,"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/T10320","display_name":"Neural Networks and Applications","score":0.9934999942779541,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.993399977684021,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.7237158417701721},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7166496515274048},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5924455523490906},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5296551585197449},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.518835186958313},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.48450466990470886},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.47760674357414246},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.4714532792568207},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.44984671473503113},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4237213730812073},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.41380560398101807},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2226043939590454},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.08641955256462097}],"concepts":[{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.7237158417701721},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7166496515274048},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5924455523490906},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5296551585197449},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.518835186958313},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.48450466990470886},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.47760674357414246},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.4714532792568207},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.44984671473503113},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4237213730812073},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.41380560398101807},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2226043939590454},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.08641955256462097},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.0},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vlsicircuits18222.2020.9162784","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vlsicircuits18222.2020.9162784","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Symposium on VLSI Circuits","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.49000000953674316,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2146501959","https://openalex.org/W2378517017","https://openalex.org/W2075470739","https://openalex.org/W981988864","https://openalex.org/W3121470121","https://openalex.org/W2172275095","https://openalex.org/W2255115219","https://openalex.org/W2038626737","https://openalex.org/W2151635921","https://openalex.org/W1972981664"],"abstract_inverted_index":{"This":[0],"paper":[1],"describes":[2],"a":[3,22],"16nm":[4],"programmable":[5],"accelerator":[6],"for":[7],"unsupervised":[8],"probabilistic":[9,18],"machine":[10],"perception":[11],"tasks":[12],"that":[13],"performs":[14,35],"Bayesian":[15],"inference":[16,38],"on":[17,52],"models":[19],"mapped":[20],"onto":[21],"2D":[23],"Markov":[24],"Random":[25],"Field,":[26],"using":[27],"MCMC.":[28],"Exploiting":[29],"two":[30],"degrees":[31],"of":[32],"parallelism,":[33],"it":[34,73],"Gibbs":[36],"sampling":[37],"at":[39,75,80],"up":[40],"to":[41],"1380\u00d7":[42],"faster":[43,58],"with":[44,59],"1965\u00d7":[45],"less":[46,61],"energy":[47,62],"than":[48,63],"an":[49,64],"Arm":[50],"Cortex-A53":[51],"the":[53,68],"same":[54,69],"SoC,":[55],"and":[56],"1.5\u00d7":[57],"6.3\u00d7":[60],"embedded":[65],"FPGA":[66],"in":[67],"technology.":[70],"At":[71],"0.8V,":[72],"runs":[74],"450MHz,":[76],"producing":[77],"44.6":[78],"MSamples/s":[79],"0.88":[81],"nJ/sample.":[82]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
