  Analog computing hardwares, such as Processing-in-memory (PIM) accelerators,
have gradually received more attention for accelerating the neural network
computations. However, PIM accelerators often suffer from intrinsic noise in
the physical components, making it challenging for neural network models to
achieve the same performance as on the digital hardware. Previous works in
mitigating intrinsic noise assumed the knowledge of the noise model, and
retraining the neural networks accordingly was required. In this paper, we
propose a noise-agnostic method to achieve robust neural network performance
against any noise setting. Our key observation is that the degradation of
performance is due to the distribution shifts in network activations, which are
caused by the noise. To properly track the shifts and calibrate the biased
distributions, we propose a "noise-aware" batch normalization layer, which is
able to align the distributions of the activations under variational noise
inherent in the analog environments. Our method is simple, easy to implement,
general to various noise settings, and does not need to retrain the models. We
conduct experiments on several tasks in computer vision, including
classification, object detection and semantic segmentation. The results
demonstrate the effectiveness of our method, achieving robust performance under
a wide range of noise settings, more reliable than existing methods. We believe
that our simple yet general method can facilitate the adoption of analog
computing devices for neural networks.
