Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Model

Abstract

Recently, there has been a trend of shifting the execution of deep learning inference tasks toward the edge of the network, closer to the user, to reduce latency and preserve data privacy. At the same time, growing interest is being devoted to the energetic sustainability of machine learning. At the intersection of these trends, in this paper we focus on the energetic characterization of machine learning at the edge, which is attracting increasing attention. Unfortunately, calculating the energy consumption of a given neural network during inference is complicated by the heterogeneity of the possible underlying hardware implementation. In this work, we aim at profiling the energetic consumption of inference tasks for some modern edge nodes by deriving simple but accurate models. To this end, we performed a large number of experiments to collect the energy consumption of fully connected and convolutional layers on two well-known edge boards by NVIDIA, namely, Jetson TX2 and Xavier. From these experimental measurements, we have then distilled a simple and practical model that can provide an estimate of the energy consumption of a certain inference task on these edge computers. We believe that this model can prove useful in many contexts as, for instance, to guide the search for efficient neural network architectures, as a heuristic in neural network pruning, to find energy-efficient offloading strategies in a split computing context, or to evaluate and compare the energy performance of deep neural network architectures.