Editor’s note:
When dealing with edge devices, diverse power and compute constraints impose tradeoffs among performance, accuracy, and energy requirements during inference. Thus, ensemble learning (multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone) has been a popular emergent paradigm for accuracy improvement in such scenarios. In cases where powering of the edge devices varies, various ensemble models can be dynamically adopted to obtain the best performance-to-power ratio. This article describes how an agnostic to the base network method can adaptively boost the performance depending on energy availability. —Theocharis Theocharides, University of Cyprus —Muhammad Shafique, Technische Universitat Wien
