D2NN: a fine-grained dual modular redundancy framework for deep neural networks

Yu Li, Yannan Liu, Min Li, Ye Tian, Bo Luo, Qiang Xu

Abstract: Deep Neural Networks (DNNs) have attracted mainstream adoption in various application domains. Their reliability and security are therefore serious concerns in those safety-critical applications such as surveillance and medical systems. In this paper, we propose a novel dual modular redundancy framework for DNNs, namely D2NN, which is able to tradeoff the system robustness with overhead in a fine-grained manner. We evaluate D2NN framework with DNN models trained on MNIST and CIFAR10 datasets under fault injection attacks, and experimental results demonstrate the efficacy of our proposed solution.