Web: http://arxiv.org/abs/2201.10520

Jan. 26, 2022, 2:11 a.m. | Kaiqi Zhao, Animesh Jain, Ming Zhao

cs.LG updates on arXiv.org arxiv.org

Pruning is a promising approach to compress complex deep learning models in
order to deploy them on resource-constrained edge devices. However, many
existing pruning solutions are based on unstructured pruning, which yield
models that cannot efficiently run on commodity hardware, and require users to
manually explore and tune the pruning process, which is time consuming and
often leads to sub-optimal results. To address these limitations, this paper
presents an adaptive, activation-based, structured pruning approach to
automatically and efficiently generate small, …

arxiv cv

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