  Deep neural networks (DNNs) demonstrate outstanding performance across most
computer vision tasks. Some critical applications, such as autonomous driving
or medical imaging, also require investigation into their behavior and the
reasons behind the decisions they make. In this vein, DNN attribution consists
in studying the relationship between the predictions of a DNN and its inputs.
Attribution methods have been adapted to highlight the most relevant weights or
neurons in a DNN, allowing to more efficiently select which weights or neurons
can be pruned. However, a limitation of these approaches is that weights are
typically compared within each layer separately, while some layers might appear
as more critical than others. In this work, we propose to investigate DNN layer
importance, i.e. to estimate the sensitivity of the accuracy w.r.t.
perturbations applied at the layer level. To do so, we propose a novel dataset
to evaluate our method as well as future works. We benchmark a number of
criteria and draw conclusions regarding how to assess DNN layer importance and,
consequently, how to budgetize layers for increased DNN efficiency (with
applications for DNN pruning and quantization), as well as robustness to
hardware failure (e.g. bit swaps).
