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arXiv · 1911.04453

Structural Pruning in Deep Neural Networks: A Small-World Approach

Gokul Krishnan·Xiaocong Du·Yu Cao
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Abstract

Deep Neural Networks (DNNs) are usually over-parameterized, causing excessive memory and interconnection cost on the hardware platform. Existing pruning approaches remove secondary parameters at the end of training to reduce the model size; but without exploiting the intrinsic network property, they still require the full interconnection to prepare the network. Inspired by the observation that brain networks follow the Small-World model, we propose a novel structural pruning scheme, which includes (1) hierarchically trimming the network into a Small-World model before training, (2) training the network for a given dataset, and (3) optimizing the network for accuracy. The new scheme effectively reduces both the model size and the interconnection needed before training, achieving a locally clustered and globally sparse model. We demonstrate our approach on LeNet-5 for MNIST and VGG-16 for CIFAR-10, decreasing the number of parameters to 2.3% and 9.02% of the baseline model, respectively.

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Gokul Krishnan, Xiaocong Du, Yu Cao. 2019-11-11. Structural Pruning in Deep Neural Networks: A Small-World Approach. https://arxiv.org/abs/1911.04453

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