FAQ: Mitigating the Impact of Faults in the Weight Memory of DNN
  Accelerators through Fault-Aware Quantization
Muhammad Abdullah Hanif Muhammad Shafique
Permanent faults induced due to imperfections in the manufacturing process of
Deep Neural Network (DNN) accelerators are a major concern, as they negatively
impact the manufacturing yield of the chip fabrication process. Fault-aware
training is the state-of-the-art approach for mitigating such faults. However,
it incurs huge retraining overheads, specifically when used for large DNNs
trained on complex datasets. To address this issue, we propose a novel
Fault-Aware Quantization (FAQ) technique for mitigating the effects of stuck-at
permanent faults in the on-chip weight memory of DNN accelerators at a
negligible overhead cost compared to fault-aware retraining while offering
comparable accuracy results. We propose a lookup table-based algorithm to
achieve ultra-low model conversion time. We present extensive evaluation of the
proposed approach using five different DNNs, i.e., ResNet-18, VGG11, VGG16,
AlexNet and MobileNetV2, and three different datasets, i.e., CIFAR-10,
CIFAR-100 and ImageNet. The results demonstrate that FAQ helps in maintaining
the baseline accuracy of the DNNs at low and moderate fault rates without
involving costly fault-aware training. For example, for ResNet-18 trained on
the CIFAR-10 dataset, at 0.04 fault rate FAQ offers (on average) an increase of
76.38% in accuracy. Similarly, for VGG11 trained on the CIFAR-10 dataset, at
0.04 fault rate FAQ offers (on average) an increase of 70.47% in accuracy. The
results also show that FAQ incurs negligible overheads, i.e., less than 5% of
the time required to run 1 epoch of retraining. We additionally demonstrate the
efficacy of our technique when used in conjunction with fault-aware retraining
and show that the use of FAQ inside fault-aware retraining enables fast
accuracy recovery.