  Stochastic neural networks such as Restricted Boltzmann Machines (RBMs) have
been successfully used in applications ranging from speech recognition to image
classification. Inference and learning in these algorithms use a Markov Chain
Monte Carlo procedure called Gibbs sampling, where a logistic function forms
the kernel of this sampler. On the other side of the spectrum, neuromorphic
systems have shown great promise for low-power and parallelized cognitive
computing, but lack well-suited applications and automation procedures. In this
work, we propose a systematic method for bridging the RBM algorithm and digital
neuromorphic systems, with a generative pattern completion task as proof of
concept. For this, we first propose a method of producing the Gibbs sampler
using bio-inspired digital noisy integrate-and-fire neurons. Next, we describe
the process of mapping generative RBMs trained offline onto the IBM TrueNorth
neurosynaptic processor -- a low-power digital neuromorphic VLSI substrate.
Mapping these algorithms onto neuromorphic hardware presents unique challenges
in network connectivity and weight and bias quantization, which, in turn,
require architectural and design strategies for the physical realization.
Generative performance metrics are analyzed to validate the neuromorphic
requirements and to best select the neuron parameters for the model. Lastly, we
describe a design automation procedure which achieves optimal resource usage,
accounting for the novel hardware adaptations. This work represents the first
implementation of generative RBM inference on a neuromorphic VLSI substrate.
