D2NN: A Fine-Grained Dual Modular Redundancy Framework for Deep Neural Networks presented at ACSAC2019 2019

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

Summary : 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.