The hardware implementation of neural networks based on memristor crossbar array provides a promising paradigm for neuromorphic computing. However, the existence of memristor conductance drift harms the reliability of the deployed neural network, which seriously hinders the practical application of memristor-based neuromorphic computing. In this paper, the impact of different types of conductance drift on the weight realized by memristors is investigated and analyzed. Then, utilizing the weight uncertainty introduced by conductance drift, we propose a weight optimization method based on the Bayesian neural network, which can greatly improve the network performance. Furthermore, an ensemble approach is proposed to enhance network reliability without increasing training cost or crossbar array resources. Finally, the effectiveness of the proposed scheme is verified through a series of experiments. In addition, the proposed scheme can be easily integrated into the implementation of neuromorphic computing, which can provide a better guarantee for its large-scale application.

基于忆阻器交叉开关阵列的神经网络的硬件实现为神经形态计算提供了有希望的范例。然而，忆阻器电导漂移的存在会损害所部署的神经网络的可靠性，从而严重阻碍了基于忆阻器的神经形态计算的实际应用。本文研究和分析了不同类型的电导漂移对忆阻器实现的重量的影响。然后，利用电导漂移引入的权重不确定性，提出了一种基于贝叶斯神经网络的权重优化方法，可以大大提高网络性能。此外，提出了一种集成方法来增强网络可靠性，而不增加训练成本或交叉开关阵列资源。最后，通过一系列实验验证了所提方案的有效性。此外，所提出的方案可以很容易地集成到神经形态计算的实现中，从而可以为其大规模应用提供更好的保证。