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GPU Implementation of Bayesian Neural Network Construction for Data-Intensive Applications

Michelle Perry, Harrison B. Prosper, Anke Meyer-Baese
Department of Scientific Computing, Florida State University, Tallahassee, FL 32306, USA
Journal of Physics: Conference Series, 513, 022027, 2014

@inproceedings{perry2014gpu,

   title={GPU Implementation of Bayesian Neural Network Construction for Data-Intensive Applications},

   author={Perry, Michelle and Prosper, Harrison B and Meyer-Baese, Anke},

   booktitle={Journal of Physics: Conference Series},

   volume={513},

   number={2},

   pages={022027},

   year={2014},

   organization={IOP Publishing}

}

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We describe a graphical processing unit (GPU) implementation of the Hybrid Markov Chain Monte Carlo (HMC) method for training Bayesian Neural Networks (BNN). Our implementation uses NVIDIA’s parallel computing architecture, CUDA. We briefly review BNNs and the HMC method and we describe our implementations and give preliminary results.
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