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.

翻译：贝叶斯神经网络能够内在估计预测不确定性，有助于做出明智决策。基于丢弃法的贝叶斯神经网络越来越多地被部署在基于自旋电子学的存算一体架构中，用于资源受限但高性能的安全关键型应用。尽管不确定性估计至关重要，但丢弃法生成和贝叶斯神经网络计算的可靠性对于目标应用同样重要，却在现有工作中被忽视。然而，由于贝叶斯神经网络的随机性，其测试难度远高于传统神经网络。本文首次建立了基于自旋电子学的丢弃法模块的非理想性模型，并分析了它们对不确定性估计和准确性的影响。此外，我们提出了一种基于可重复性排序的贝叶斯神经网络测试框架，最高可实现$100\%$的故障覆盖率，并且仅使用$0.2\%$的训练数据作为测试向量。