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.

本文提出一种新的精确剪枝技术，利用有效的最优传输方案，使得算法自动调整探索 - 开发行为，从而在 3 个不同的数据集、5 个不同的模型、各种剪枝比例和两种稀疏度预算和剪枝颗粒度等方面均取得了先进的性能。