  Deep Neural Networks (DNNs) and their accelerators are being deployed ever
more frequently in safety-critical applications leading to increasing
reliability concerns. A traditional and accurate method for assessing DNNs'
reliability has been resorting to fault injection, which, however, suffers from
prohibitive time complexity. While analytical and hybrid fault
injection-/analytical-based methods have been proposed, they are either
inaccurate or specific to particular accelerator architectures. In this work,
we propose a novel accurate, fine-grain, metric-oriented, and
accelerator-agnostic method called DeepVigor that provides vulnerability value
ranges for DNN neurons' outputs. An outcome of DeepVigor is an analytical model
representing vulnerable and non-vulnerable ranges for each neuron that can be
exploited to develop different techniques for improving DNNs' reliability.
Moreover, DeepVigor provides reliability assessment metrics based on
vulnerability factors for bits, neurons, and layers using the vulnerability
ranges. The proposed method is not only faster than fault injection but also
provides extensive and accurate information about the reliability of DNNs,
independent from the accelerator. The experimental evaluations in the paper
indicate that the proposed vulnerability ranges are 99.9% to 100% accurate even
when evaluated on previously unseen test data. Also, it is shown that the
obtained vulnerability factors represent the criticality of bits, neurons, and
layers proficiently. DeepVigor is implemented in the PyTorch framework and
validated on complex DNN benchmarks.
