Bayesian Neural Networks (BayNNs) can inherently estimate predictive
uncertainty, facilitating informed decision-making. Dropout-based BayNNs are
increasingly implemented in spintronics-based computation-in-memory
architectures for resource-constrained yet high-performance safety-critical
applications. Although uncertainty estimation is important, the reliability of
Dropout generation and BayNN computation is equally important for target
applications but is overlooked in existing works. However, testing BayNNs is
significantly more challenging compared to conventional NNs, due to their
stochastic nature. In this paper, we present for the first time the model of
the non-idealities of the spintronics-based Dropout module and analyze their
impact on uncertainty estimates and accuracy. Furthermore, we propose a testing
framework based on repeatability ranking for Dropout-based BayNN with up to
$100\%$ fault coverage while using only $0.2\%$ of training data as test
vectors.

Bayesian Neural Networks (BayNNs) and uncertainty estimation in spintronics-based computation-in-memory architectures are analyzed with a focus on the reliability of Dropout generation and BayNN computation, proposing a testing framework for Dropout-based BayNN with high fault coverage and minimal training data usage.