Restricted Boltzmann Machines and Deep Belief Networks have been successfully
used in a wide variety of applications including image classification and
speech recognition. Inference and learning in these algorithms uses a Markov
Chain Monte Carlo procedure called Gibbs sampling. A sigmoidal function forms
the kernel of this sampler which can be realized from the firing statistics of
noisy integrate-and-fire neurons on a neuromorphic VLSI substrate. This paper
demonstrates such an implementation on an array of digital spiking neurons with
stochastic leak and threshold properties for inference tasks and presents some
key performance metrics for such a hardware-based sampler in both the
generative and discriminative contexts.Comment: Accepted at ISCAS 201