  Today's intelligent applications can achieve high performance accuracy using
machine learning (ML) techniques, such as deep neural networks (DNNs).
Traditionally, in a remote DNN inference problem, an edge device transmits raw
data to a remote node that performs the inference task. However, this may incur
high transmission energy costs and puts data privacy at risk. In this paper, we
propose a technique to reduce the total energy bill at the edge device by
utilizing model compression and time-varying model split between the edge and
remote nodes. The time-varying representation accounts for time-varying
channels and can significantly reduce the total energy at the edge device while
maintaining high accuracy (low loss). We implement our approach in an image
classification task using the MNIST dataset, and the system environment is
simulated as a trajectory navigation scenario to emulate different channel
conditions. Numerical simulations show that our proposed solution results in
minimal energy consumption and $CO_2$ emission compared to the considered
baselines while exhibiting robust performance across different channel
conditions and bandwidth regime choices.
