To deploy deep learning algorithms on resource-limited scenarios, an emerging
device-resistive random access memory (ReRAM) has been regarded as promising
via analog computing. However, the practicability of ReRAM is primarily limited
due to the weight drifting of ReRAM neural networks due to multi-factor
reasons, including manufacturing, thermal noises, and etc. In this paper, we
propose a novel Bayesian optimization method for fault tolerant neural network
architecture (BayesFT). For neural architecture search space design, instead of
conducting neural architecture search on the whole feasible neural architecture
search space, we first systematically explore the weight drifting tolerance of
different neural network components, such as dropout, normalization, number of
layers, and activation functions in which dropout is found to be able to
improve the neural network robustness to weight drifting. Based on our
analysis, we propose an efficient search space by only searching for dropout
rates for each layer. Then, we use Bayesian optimization to search for the
optimal neural architecture robust to weight drifting. Empirical experiments
demonstrate that our algorithmic framework has outperformed the
state-of-the-art methods by up to 10 times on various tasks, such as image
classification and object detection.

本文提出了使用贝叶斯优化来提高深度神经网络抗重量漂移性能的方法，并使用 dropout 来提高网络的鲁棒性。实验结果表明该方法在各种任务（例如图像分类和目标检测）中优于现有技术。