  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.
