High-throughput bayesian computing machine with reconfigurable hardware

@inproceedings{Lin2010HighthroughputBC,
  title={High-throughput bayesian computing machine with reconfigurable hardware},
  author={Mingjie Lin and Ilia A. Lebedev and John Wawrzynek},
  booktitle={FPGA},
  year={2010}
}
We use reconfigurable hardware to construct a high throughput Bayesian computing machine (BCM) capable of evalu- ating probabilistic networks with arbitrary DAG (directed acyclic graph) topology. Our BCM achieves high throughput by exploiting the FPGA's distributed memories and abundant hardware structures (such as long carry-chains and registers), which enables us to 1) develop an innovative memory allocation scheme based on a maximal matching algorithm that completely avoids memory stalls, 2… CONTINUE READING

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  • 2006
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