  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.
