  Approximate computing is known for enhancing deep neural network
accelerators' energy efficiency by introducing inexactness with a tolerable
accuracy loss. However, small accuracy variations may increase the sensitivity
of these accelerators towards undesired subtle disturbances, such as permanent
faults. The impact of permanent faults in accurate deep neural network (AccDNN)
accelerators has been thoroughly investigated in the literature. Conversely,
the impact of permanent faults and their mitigation in approximate DNN (AxDNN)
accelerators is vastly under-explored. Towards this, we first present an
extensive fault resilience analysis of approximate multi-layer perceptrons
(MLPs) and convolutional neural networks (CNNs) using the state-of-the-art
Evoapprox8b multipliers in GPU and TPU accelerators. Then, we propose a novel
fault mitigation method, i.e., fault-aware retuning of weights (Fal-reTune).
Fal-reTune retunes the weights using a weight mapping function in the presence
of faults for improved classification accuracy. To evaluate the fault
resilience and the effectiveness of our proposed mitigation method, we used the
most widely used MNIST, Fashion-MNIST, and CIFAR10 datasets. Our results
demonstrate that the permanent faults exacerbate the accuracy loss in AxDNNs
compared to the AccDNN accelerators. For instance, a permanent fault in AxDNNs
can lead to 56\% accuracy loss, whereas the same faulty bit can lead to only
4\% accuracy loss in AccDNN accelerators. We empirically show that our proposed
Fal-reTune mitigation method improves the performance of AxDNNs up to 98%, even
with fault rates of up to 50%. Furthermore, we observe that the fault
resilience in AxDNNs is orthogonal to their energy efficiency.
