Binary Bayesian Neural Networks for Efficient Uncertainty Estimation Leveraging Inherent Stochasticity of Spintronic Devices

Abstract

In the age of automation, machine learning systems for real-time critical decisions in various domains such as autonomous driving are at an all-time high. Predictive uncertainty allows a machine learning system to make more insightful decisions by avoiding blind predictions. Algorithmically, Bayesian neural networks (BayNNs) based on dropout are principled methods for estimating predictive uncertainty in a machine learning application. However, the computational cost and power consumption make the use of BayNNs on embedded hardware unattractive. Hardware accelerators with emerging non-volatile resistive memories (NVMs) such as Magnetic Tunnel Junction (MTJ) in conjunction with quantized models are an interesting option for efficient implementations of such a system. Binary BayNNs are a desirable alternative that can provide predictive uncertainty efficiently by combining the benefits of quantization and hardware acceleration. In this paper, propose for the first time the binary bayesian neural network (BayBNN) using dropout-based approximation, and we leverage the inherent randomness of spin-tronic devices for in-memory Bayesian inference. Our proposed method can detect up-to 100% of the out-of-distribution data, improve inference accuracy by 15% for corrupted data, and ~ 2% for in-distribution data.