Dynamic Ensemble Inference at the Edge

We propose a dynamic resource allocation algorithm in the context of future wireless networks endowed with edge computing, to enable accurate energy efficient classification with end-to-end delay guarantees. In our scenario, sensor devices continuously upload data to an Edge Server (ES) for classification purposes. Merging Lyapunov stochastic optimization and ensemble inference, we propose DEsIreE, a low-complexity method that dynamically selects the data quantization level, the device transmit power, and the ES's CPU scheduling, without any prior knowledge of the statistics of wireless channels and data arrivals. Numerical simulations run on two real datasets assess the effectiveness of our algorithm in optimizing sensors' energy consumption and classification accuracy, with the ensemble yielding considerable gain.