Show simple item record

dc.contributor.authorMingas, Grigoriosen
dc.contributor.authorBottolo, Leonardoen
dc.contributor.authorBouganis, Christos-Savvasen
dc.date.accessioned2017-01-12T12:36:23Z
dc.date.available2017-01-12T12:36:23Z
dc.date.issued2017-04en
dc.identifier.issn0888-613X
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/261839
dc.description.abstractParticle Markov Chain Monte Carlo (pMCMC) is a stochastic algorithm designed to generate samples from a probability distribution, when the density of the distribution does not admit a closed form expression. pMCMC is most commonly used to sample from the Bayesian posterior distribution in State-Space Models (SSMs), a class of probabilistic models used in numerous scientific applications. Nevertheless, this task is prohibitive when dealing with complex SSMs with massive data, due to the high computational cost of pMCMC and its poor performance when the posterior exhibits multi-modality. This paper aims to address both issues by: 1) Proposing a novel pMCMC algorithm (denoted ppMCMC), which uses multiple Markov chains (instead of the one used by pMCMC) to improve sampling efficiency for multi-modal posteriors, 2) Introducing custom, parallel hardware architectures, which are tailored for pMCMC and ppMCMC. The architectures are implemented on Field Programmable Gate Arrays (FPGAs), a type of hardware accelerator with massive parallelization capabilities. The new algorithm and the two FPGA architectures are evaluated using a large-scale case study from genetics. Results indicate that ppMCMC achieves 1.96x higher sampling efficiency than pMCMC when using sequential CPU implementations. The FPGA architecture of pMCMC is 12.1x and 10.1x faster than state-of-the-art, parallel CPU and GPU implementations of pMCMC and up to 53x more energy efficient; the FPGA architecture of ppMCMC increases these speedups to 34.9x and 41.8x respectively and is 173x more power efficient, bringing previously intractable SSM-based data analyses within reach.
dc.description.sponsorshipThe authors would like to thank the Wellcome Trust (Grant reference 097816/Z/11/A) and the EPSRC (Grant reference EP/I012036/1) for the financial support given to this research project.
dc.format.mediumPrinten
dc.languageengen
dc.language.isoenen
dc.publisherElsevier
dc.rightsAttribution 4.0 Internationalen
dc.rightsAttribution 4.0 Internationalen
dc.rightsAttribution 4.0 Internationalen
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleParticle MCMC algorithms and architectures for accelerating inference in state-space models.en
dc.typeArticle
prism.endingPage433
prism.publicationDate2017en
prism.publicationNameInternational journal of approximate reasoning : official publication of the North American Fuzzy Information Processing Societyen
prism.startingPage413
prism.volume83en
dc.identifier.doi10.17863/CAM.7060
dcterms.dateAccepted2016-10-24en
rioxxterms.versionofrecord10.1016/j.ijar.2016.10.011en
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2017-04en
dc.contributor.orcidBottolo, Leonardo [0000-0002-6381-2327]
dc.identifier.eissn1873-4731
rioxxterms.typeJournal Article/Reviewen
cam.orpheus.successThu Jan 30 12:56:38 GMT 2020 - The item has an open VoR version.*
rioxxterms.freetoread.startdate2100-01-01


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International