Late breaking results: adaptive ensembles of dynamic DNNs for collaborative edge inference

Edge computing enables low-latency and privacy-preserving DNN inference, yet heterogeneous and dynamically changing device resources make it difficult to satisfy real-time constraints. In this paper, we present AdaEnsemble, an adaptive and collaborative ensemble inference framework that integrates Dynamic DNNs with deadline-aware scheduling. The system profiles accuracy and latency offline and selects both model widths and participating devices at runtime to maximize accuracy under a given deadline. Experiments on heterogeneous edge devices show that AdaEnsemble adapts effectively to different latency requirements and consistently outperforms the state-of-art.