Bayesian Neural Network Realization by Exploiting Inherent Stochastic Characteristics of Analog RRAM

Abstract

For the first time, this paper develops a novel stochastic computing method by utilizing the inherent random noises of analog RRAM. With the designed analog switching characteristics, the RRAM device can realize the function of sampling from a tunable probabilistic distribution. A Bayesian neural network (BayNN), whose weights are represented by probability distributions, is experimentally demonstrated on the fabricated 160K RRAM array. The measured result achieves 97% accuracy for image classification on MNIST dataset. Moreover, the RRAM based BayNN shows anti-attack capability with inherent device stochastic behavior to detect "adversarial" images, which are generated by adding noises to the original MNIST images and can fool the conventional deep neural networks. This is the first demonstration work for the widely-used BayNN algorithms with emerging devices.