  Deep learning models undergo a significant increase in the number of
parameters they possess, leading to the execution of a larger number of
operations during inference. This expansion significantly contributes to higher
energy consumption and prediction latency. In this work, we propose EAT, a
gradient-based algorithm that aims to reduce energy consumption during model
training. To this end, we leverage a differentiable approximation of the
$\ell_0$ norm, and use it as a sparse penalty over the training loss. Through
our experimental analysis conducted on three datasets and two deep neural
networks, we demonstrate that our energy-aware training algorithm EAT is able
to train networks with a better trade-off between classification performance
and energy efficiency.
