  Deep learning algorithms are increasingly employed at the edge. However, edge
devices are resource constrained and thus require efficient deployment of deep
neural networks. Pruning methods are a key tool for edge deployment as they can
improve storage, compute, memory bandwidth, and energy usage. In this paper we
propose a novel accurate pruning technique that allows precise control over the
output network size. Our method uses an efficient optimal transportation scheme
which we make end-to-end differentiable and which automatically tunes the
exploration-exploitation behavior of the algorithm to find accurate sparse
sub-networks. We show that our method achieves state-of-the-art performance
compared to previous pruning methods on 3 different datasets, using 5 different
models, across a wide range of pruning ratios, and with two types of sparsity
budgets and pruning granularities.
