Pruning Neural Networks with Interpolative Decompositions

• URL: http://arxiv.org/abs/2108.00065v1
• Date: Fri, 30 Jul 2021 20:13:49 GMT
• Title: Pruning Neural Networks with Interpolative Decompositions
• Authors: Jerry Chee, Megan Renz, Anil Damle, Chris De Sa
• Abstract summary: We introduce a principled approach to neural network pruning that casts the problem as a structured low-rank matrix approximation. We demonstrate how to prune a neural network by first building a set of primitives to prune a single fully connected or convolution layer. We achieve an accuracy of 93.62 $pm$ 0.36% using VGG-16 on CIFAR-10, with a 51% FLOPS reduction.
• Score: 5.377278489623063
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a principled approach to neural network pruning that casts the problem as a structured low-rank matrix approximation. Our method uses a novel application of a matrix factorization technique called the interpolative decomposition to approximate the activation output of a network layer. This technique selects neurons or channels in the layer and propagates a corrective interpolation matrix to the next layer, resulting in a dense, pruned network with minimal degradation before fine tuning. We demonstrate how to prune a neural network by first building a set of primitives to prune a single fully connected or convolution layer and then composing these primitives to prune deep multi-layer networks. Theoretical guarantees are provided for pruning a single hidden layer fully connected network. Pruning with interpolative decompositions achieves strong empirical results compared to the state-of-the-art on multiple applications from one and two hidden layer networks on Fashion MNIST to VGG and ResNets on CIFAR-10. Notably, we achieve an accuracy of 93.62 $\pm$ 0.36% using VGG-16 on CIFAR-10, with a 51% FLOPS reduction. This gains 0.02% from the full-sized model.

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