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arXiv · 2105.04218

Exploiting Elasticity in Tensor Ranks for Compressing Neural Networks

Jie Ran·Rui Lin·Hayden K. H. So·Graziano Chesi·Ngai Wong
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Abstract

Elasticities in depth, width, kernel size and resolution have been explored in compressing deep neural networks (DNNs). Recognizing that the kernels in a convolutional neural network (CNN) are 4-way tensors, we further exploit a new elasticity dimension along the input-output channels. Specifically, a novel nuclear-norm rank minimization factorization (NRMF) approach is proposed to dynamically and globally search for the reduced tensor ranks during training. Correlation between tensor ranks across multiple layers is revealed, and a graceful tradeoff between model size and accuracy is obtained. Experiments then show the superiority of NRMF over the previous non-elastic variational Bayesian matrix factorization (VBMF) scheme.

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Jie Ran, Rui Lin, Hayden K. H. So, Graziano Chesi, Ngai Wong. 2021-05-10. Exploiting Elasticity in Tensor Ranks for Compressing Neural Networks. https://arxiv.org/abs/2105.04218

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