NDLI: A Custom Precision Based Architecture for Accelerating Parallel Tempering MCMC on FPGAs without Introducing Sampling Error

Content Provider	IEEE Xplore Digital Library
Author	Mingas, G. Bouganis, C.-S.
Copyright Year	2012
Abstract	Markov Chain Monte Carlo (MCMC) is a method used to draw samples from probability distributions in order to estimate - otherwise intractable - integrals. When the distribution is complex, simple MCMC becomes inefficient and advanced, computationally intensive MCMC methods are employed to make sampling possible. This work proposes a novel streaming FPGA architecture to accelerate Parallel Tempering, a widely adopted MCMC method designed to sample from multimodal distributions. The proposed architecture demonstrates how custom precision can be intelligently employed without introducing sampling errors, in order to save resources and increase the sampling throughg put. Speedups of up to two orders of magnitude compared to software and 1.53x-76.88x compared to a GPGPU implementation are achieved when performing Bayesian inference for a mixture model.
Starting Page	153
Ending Page	156
File Size	181927
Page Count	4
File Format	PDF
ISBN	9781467316057
DOI	10.1109/FCCM.2012.34
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-04-29
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Field programmable gate arrays Computer architecture Pipelines Pipeline processing Acceleration Throughput Bayesian methods FPGA Markov Chain Monte Carlo Parallel Tempering
Content Type	Text
Resource Type	Article

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