Scalable and Efficient Methods for Uncertainty Estimation and Reduction in Deep Learning

TL;DR

The paper addresses reliability of neural networks in safety-critical, resource-constrained environments by tackling uncertainty from out-of-distribution inputs and CIM hardware non-idealities. It introduces problem-aware training, novel NN topologies, and hardware co-design on Computation-in-Memory (CIM) with resistive memristors to enable scalable, energy-efficient uncertainty estimation. Key contributions include dropout-based binary BayNNs on spintronic CIM achieving up to $100\%$ OOD detection and substantial energy savings, VI-based BayNNs with selective Bayesian treatment delivering major power and memory reductions, and SpinBayes for improved accuracy and uncertainty with modest RNG requirements; plus comprehensive testing and reliability techniques such as approximate test vector compaction, one-shot testing, self-testing, scrubbing, re-calibration, ECC-based fault tolerance, and self-healing methods. The integrated results demonstrate significant gains in inference accuracy, uncertainty quantification, OOD detection, and energy efficiency, highlighting CIM-based NN viability for safety-critical applications.

Abstract

Neural networks (NNs) can achieved high performance in various fields such as computer vision, and natural language processing. However, deploying NNs in resource-constrained safety-critical systems has challenges due to uncertainty in the prediction caused by out-of-distribution data, and hardware non-idealities. To address the challenges of deploying NNs in resource-constrained safety-critical systems, this paper summarizes the (4th year) PhD thesis work that explores scalable and efficient methods for uncertainty estimation and reduction in deep learning, with a focus on Computation-in-Memory (CIM) using emerging resistive non-volatile memories. We tackle the inherent uncertainties arising from out-of-distribution inputs and hardware non-idealities, crucial in maintaining functional safety in automated decision-making systems. Our approach encompasses problem-aware training algorithms, novel NN topologies, and hardware co-design solutions, including dropout-based \emph{binary} Bayesian Neural Networks leveraging spintronic devices and variational inference techniques. These innovations significantly enhance OOD data detection, inference accuracy, and energy efficiency, thereby contributing to the reliability and robustness of NN implementations.