Calibrated BatchNorm: Improving Robustness Against Noisy Weights in Neural Networks

Analog computing hardware has gradually received more attention by the researchers for accelerating the neural network computations in recent years. However, the analog accelerators often suffer from the undesirable intrinsic noise caused by the physical components, making the neural networks challenging to achieve ordinary performance as on the digital ones. We suppose the performance drop of the noisy neural networks is due to the distribution shifts in the network activations. In this paper, we propose to recalculate the statistics of the batch normalization layers to calibrate the biased distributions during the inference phase. Without the need of knowing the attributes of the noise beforehand, our approach is able to align the distributions of the activations under variational noise inherent in the analog environments. In order to validate our assumptions, we conduct quantitative experiments and apply our methods on several computer vision tasks, including classification, object detection, and semantic segmentation. The results demonstrate the effectiveness of achieving noise-agnostic robust networks and progress the developments of the analog computing devices in the field of neural networks.