  Energy-efficient deep neural network (DNN) accelerators are prone to
non-idealities that degrade DNN performance at inference time. To mitigate such
degradation, existing methods typically add perturbations to the DNN weights
during training to simulate inference on noisy hardware. However, this often
requires knowledge about the target hardware and leads to a trade-off between
DNN performance and robustness, decreasing the former to increase the latter.
In this work, we show that applying sharpness-aware training, by optimizing for
both the loss value and loss sharpness, significantly improves robustness to
noisy hardware at inference time without relying on any assumptions about the
target hardware. In particular, we propose a new adaptive sharpness-aware
method that conditions the worst-case perturbation of a given weight not only
on its magnitude but also on the range of the weight distribution. This is
achieved by performing sharpness-aware minimization scaled by outlier
minimization (SAMSON). Our approach outperforms existing sharpness-aware
training methods both in terms of model generalization performance in noiseless
regimes and robustness in noisy settings, as measured on several architectures
and datasets.
