Unsupervised Test-Time Adaptation of Deep Neural Networks at the Edge: A Case Study

Deep learning is being increasingly used in mobile and edge autonomous systems. The prediction accuracy of deep neural networks (DNNs), however, can degrade after deployment due to encountering data samples whose distributions are differ-ent than the training samples. To continue to robustly predict, DNNs must be able to adapt themselves post-deployment. Such adaptation at the edge is challenging as new labeled data may not be available, and it has to be performed on a resource-constrained device. This paper performs a case study to evaluate the cost of test-time fully unsupervised adaptation strategies on a real-world edge platform: Nvidia Jetson Xavier NX. In particular, we adapt pretrained state-of-the-art robust DNNs (trained using data augmentation) to improve the accuracy on image classification data that contains various image corruptions. During this prediction-time on-device adaptation, the model parameters of a DNN are updated using a single backpropagation pass while optimizing entropy loss. The effects of following three simple model updates are compared in terms of accuracy, adaptation time and energy: updating only convolutional (Conv-Tune); only fully-connected (FC-Tune); and only batch-norm parameters (BN-Tune). Our study shows that BN-Tune and Conv-Tune are more effective than FC-Tune in terms of improving accuracy for corrupted images data (average of 6.6%, 4.97%, and 4.02%, respectively over no adaptation). However, FC-Tune leads to significantly faster and more energy efficient solution with a small loss in accuracy. Even when using FC-Tune, the extra overheads of on-device fine-tuning are significant to meet tight real-time deadlines (209ms). This study motivates the need for designing hardware-aware robust algorithms for efficient on-device adaptation at the autonomous edge.