Bayesian In-Memory Computing with Resistive Memories

This paper explores three approaches using resistive memory for Bayesian near-memory and in-memory computing, leveraging their inherent randomness. The strategies include Bayesian machines for efficient near-memory computing, Bayesian neural networks exploiting randomness of synapses, and Bayesian learning utilizing the Metropolis-Hastings Markov Chain Monte Carlo technique. These methods achieve accuracy competitive with conventional software methods and allow for the evaluation of decision uncertainty.