Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices

Abstract

The prediction accuracy of deep neural networks (DNNs) after deployment at the edge can suffer with time due to shifts in the distribution of the new data. To improve robustness of DNNs, they must be able to update themselves. However, DNN adaptation at the edge is challenging due to lack of resources. Recently, lightweight prediction-time unsupervised DNN adaptation techniques have been introduced that improve prediction accuracy of the models for noisy data by re-tuning the batch normalization parameters. This paper performs a comprehensive measurement study of such techniques to quantify their performance and energy on various edge devices as well as find bottlenecks and propose optimization opportunities.