ParaLearn: a massively parallel, scalable system for learning interaction networks on FPGAs

@inproceedings{Asadi2010ParaLearnAM,
  title={ParaLearn: a massively parallel, scalable system for learning interaction networks on FPGAs},
  author={Narges Bani Asadi and Christopher W. Fletcher and Greg Gibeling and John Wawrzynek and Wing H. Wong and Garry P. Nolan},
  booktitle={ICS},
  year={2010}
}
ParaLearn is a scalable, parallel FPGA-based system for learning interaction networks using Bayesian statistics. ParaLearn includes problem specific parallel/scalable algorithms, system software and hardware architecture to address this complex problem. Learning interaction networks from data uncovers causal relationships and allows scientists to predict and explain a system's behavior. Interaction networks have applications in many fields, though we will discuss them particularly in the field… CONTINUE READING

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