title: Bayesian Estimation and Inference Using Stochastic Electronics.
abstract: In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream.
authors.affiliation: Biomedical Engineering and Neuroscience, The MARCS Institute, Western Sydney University Sydney, NSW, Australia.
authors.forename: Chetan Singh
authors.initials: CS
authors.lastname: Thakur
authors.affiliation: Biomedical Engineering and Neuroscience, The MARCS Institute, Western Sydney University Sydney, NSW, Australia.
authors.forename: Saeed
authors.initials: S
authors.lastname: Afshar
authors.affiliation: Biomedical Engineering and Neuroscience, The MARCS Institute, Western Sydney University Sydney, NSW, Australia.
authors.forename: Runchun M
authors.initials: RM
authors.lastname: Wang
authors.affiliation: Biomedical Engineering and Neuroscience, The MARCS Institute, Western Sydney University Sydney, NSW, Australia.
authors.forename: Tara J
authors.initials: TJ
authors.lastname: Hamilton
authors.affiliation: Biomedical Engineering and Neuroscience, The MARCS Institute, Western Sydney University Sydney, NSW, Australia.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Tapson
authors.affiliation: Biomedical Engineering and Neuroscience, The MARCS Institute, Western Sydney University Sydney, NSW, Australia.
authors.forename: André
authors.initials: A
authors.lastname: van Schaik
keywords: Bayesian inference; Hidden Markov models; Sequential Monte Carlo sampling; direct acyclic graph; neuromorphic engineering; probabilistic graphical models; spiking neural networks; stochastic computation