On population-based simulation for static inference
Statistics and Computing
Gaussian random number generators
ACM Computing Surveys (CSUR)
Reconfigurable computing for learning Bayesian networks
Proceedings of the 16th international ACM/SIGDA symposium on Field programmable gate arrays
A decentralized parallel implementation for parallel tempering algorithm
Parallel Computing
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
A method for training finite mixture models under a fuzzy clustering principle
Fuzzy Sets and Systems
Designing Custom Arithmetic Data Paths with FloPoCo
IEEE Design & Test
A Run-Time Adaptive FPGA Architecture for Monte Carlo Simulations
FPL '11 Proceedings of the 2011 21st International Conference on Field Programmable Logic and Applications
Design of a financial application driven multivariate gaussian random number generator for an FPGA
ARC'10 Proceedings of the 6th international conference on Reconfigurable Computing: architectures, Tools and Applications
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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.