A Memristor Circuit Implementing Tunable Stochastic Distributions for Bayesian Inference and Monte Carlo Sampling

In this paper we present a novel memristive circuit that is capable of generating tunable stochastic distributions. The proposed circuit leverages the inherent read noise of the memristor and utilises feedback to shape its spectrum into achieving control over the output distributions mean and standard deviation. We analyse the relationship between various loop parameters and the output noise characteristics of the circuit. We experimentally build the circuit and investigate the output distributions for a range of circuit parameters. Lastly, we develop the theory, propose a system and demonstrate an example, where such circuits generate tunable distributions in hardware for Bayesian Inference and Monte-Carlo sampling.