Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Computational Statistics & Data Analysis - Nonlinear methods and data mining
BEE2: A High-End Reconfigurable Computing System
IEEE Design & Test
Borph: an operating system for fpga-based reconfigurable computers
Borph: an operating system for fpga-based reconfigurable computers
Causal discovery from a mixture of experimental and observational data
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
High-throughput bayesian computing machine with reconfigurable hardware
Proceedings of the 18th annual ACM/SIGDA international symposium on Field programmable gate arrays
Towards program optimization through automated analysis of numerical precision
Proceedings of the 8th annual IEEE/ACM international symposium on Code generation and optimization
High-throughput Bayesian network learning using heterogeneous multicore computers
Proceedings of the 24th ACM International Conference on Supercomputing
Bridging the GPGPU-FPGA efficiency gap
Proceedings of the 19th ACM/SIGDA international symposium on Field programmable gate arrays
Parallel tempering MCMC acceleration using reconfigurable hardware
ARC'12 Proceedings of the 8th international conference on Reconfigurable Computing: architectures, tools and applications
Parallel globally optimal structure learning of Bayesian networks
Journal of Parallel and Distributed Computing
FPGA implementation of particle swarm optimization for Bayesian network learning
Computers and Electrical Engineering
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Learning the structure of Bayesian networks(BNs) is known to be NP-complete and most of the recent work in the field is based on heuristics. Many recent approaches to the problem trade correctness and exactness for faster computation and are still computationally infeasible, except for networks with few variables. In this paper we present a software/hardware co-design approach to learning Bayesian networks from experimental data that is scalable to very large networks. Our implementation improves the performance of algorithms that are traditionally developed based on the Von Neumann computing paradigm by more than four orders of magnitude. Through parallel implementation and exploitation of the reconfigurability of Field Programmable Gate Array (FPGA) systems our design enables scientists to apply BN learning techniques to large problems such as studies in molecular biology where the number of variables in the system overwhelms any state of the art software implementations. We describe how we combine Markov Chain Monte Carlo (MCMC) sampling with Bayesian network learning techniques as well as supervised learning methods in a parallel and scalable design. We also present how our design is mapped and customized to run on the Berkeley Emulation Engine 2 (BEE2) multi-FPGA system. Experimental results are presented on synthetic data sets generated from standard Bayesian networks as well as a real life problem in the context of systems biology