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Deep Belief Nets (DBNs) are an emerging application in the machine learning domain, which use Restricted Boltzmann Machines (RBMs) as their basic building block. Although small scale DBNs have shown great potential, the computational cost of RBM training has been a major challenge in scaling to large networks. In this paper, the authors present a highly scalable architecture for Deep Belief Net processing on hardware systems that can handle hundreds of boards, if not more, of customized logic with near linear performance increase. They elucidate tradeoffs between flexibility in the neuron connections, and the hardware resources, such as memory and communication bandwidth, required to build a custom processor design that has optimal efficiency.
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