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Abstract: hardware transient faults are proven to have a significant impact on deep neural networks (DNNs), whose safety-critical misclassification (SCM) in autonomous vehicles, healthcare, and space applications is increased up to four times. However, the inaccuracy evaluation using accurate fault injection is time-consuming and requires several hours and even a couple of days on a complete simulation platform. To accelerate the evaluation of hardware transient faults on DNNs, we design a unified and end-to-end automatic methodology, A-Mean, using the silent data corruption (SDC) rate of basic operations (such as convolution, addition, multiply, ReLU, and max-pooling) and a static two-level mean calculation mechanism to rapidly compute the overall SDC rate, for estimating the general classification metric accuracy and application-specific metric SCM. More importantly, a max-policy is used to determine the SDC boundary of non-sequential structures in DNNs. Then, the worst-case scheme is used to further calculate the enlarged SCM and halved accuracy under transient faults, via merging the static results of SDC with the original data from one-time dynamic fault-free execution. Furthermore, all of the steps mentioned above have been implemented automatically, so that this easy-to-use automatic tool can be employed for prompt evaluation of transient faults on diverse DNNs. Meanwhile, a novel metric "fault sensitivity" is defined to characterize the variation of transient fault-induced higher SCM and lower accuracy. The comparative results with a state-of-the-art fault injection method TensorFI+ on five DNN models and four datasets show that our proposed estimation method A-Mean achieves up to 922.80 times speedup, with just 4.20% SCM loss and 0.77% accuracy loss on average. The artifact of A-Mean is publicly available at https://github.com/breatrice321/A-Meanhttps://github.com/breatrice321/A-Mean.

加速深度神经网络在硬件瞬态故障下准确性评估的端到端自动化方法

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