Name
Abstract | ||
|---|---|---|
Markov Chain Monte Carlo (MCMC) is a family of algorithms which is used to draw samples from arbitrary probability distributions in order to estimate - otherwise intractable - integrals. When the distribution is complex, simple MCMC becomes inefficient and advanced variations are employed. This paper proposes a novel FPGA architecture to accelerate Parallel Tempering, a computationally expensive, popular MCMC method, which is designed to sample from multimodal distributions. The proposed architecture can be used to sample from any distribution. Moreover, the work demonstrates that MCMC is robust to reductions in the arithmetic precision used to evaluate the sampling distribution and this robustness is exploited to improve the FPGA's performance. A 1072x speedup compared to software and a 3.84x speedup compared to a GPGPU implementation are achieved when performing Bayesian inference for a mixture model without any compromise on the quality of results, opening the way for the handling of previously intractable problems. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-28365-9_19 | ARC |
Keywords | Field | DocType |
reconfigurable hardware,bayesian inference,sampling distribution,multimodal distribution,mcmc acceleration,arbitrary probability distribution,popular mcmc method,gpgpu implementation,simple mcmc,proposed architecture,novel fpga architecture,intractable problem | Sampling distribution,Markov chain Monte Carlo,Computer science,Parallel computing,Robustness (computer science),Probability distribution,Parallel tempering,Mixture model,Speedup,Reconfigurable computing | Conference |
Citations | PageRank | References |
5 | 0.56 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Grigorios Mingas | 1 | 51 | 4.80 |
| Christos Savvas Bouganis | 2 | 400 | 49.04 |