Deep Neural Networks (DNNs) enable a wide series of technological
advancements, ranging from clinical imaging, to predictive industrial
maintenance and autonomous driving. However, recent findings indicate that
transient hardware faults may corrupt the models prediction dramatically. For
instance, the radiation-induced misprediction probability can be so high to
impede a safe deployment of DNNs models at scale, urging the need for efficient
and effective hardening solutions. In this work, we propose to tackle the
reliability issue both at training and model design time. First, we show that
vanilla models are highly affected by transient faults, that can induce a
performances drop up to 37%. Hence, we provide three zero-overhead solutions,
based on DNN re-design and re-train, that can improve DNNs reliability to
transient faults up to one order of magnitude. We complement our work with
extensive ablation studies to quantify the gain in performances of each
hardening component.

本文提出一种针对深度神经网络在训练和模型设计时提高其可靠性的解决方案，旨在解决硬件故障导致对模型预测出现错误等问题。在研究中通过 DNN 重新设计、重新训练等方式，提出三种零附加成本的解决方案，可以在一定程度上提高 DNNs 对于瞬态故障的可靠性，并通过广泛的消融研究量化了每种硬化组件的性能提升。