Computing with Uncertainty in Probabilistic Neural Networks on Silicon

This paper suggests that probab ilistic VLSI architectures may provide insights into naturally-stochastic processes,and describes a particular application to perform robust data fusion, using unsupervised feature extraction and compensation of sensor drift. Two very interesting, stochastic networks are (1) the Helmholtz machine, which uses a local learning rule and whose hidden units choose states according to a probab ility distribution, rather than deterministically; and (2) the Product-of-Experts (PoE) machine, which uses the same computational elements, but has a training algorithm that is both more reliable and more amenable to on-chip implementation. Stochasticity can be provided in hardware by sampling an oscillator’s binary output that varies its mark-to-period ratio, provided that the outputs of a set of oscillators remains uncorrelated.