Differentiable Learning of Generalized Structured Matrices for Efficient Deep Neural Networks

Published: 16 Jan 2024, Last Modified: 08 Mar 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: Structured Matrix, Block Low Rank, Low Rank, Efficient Neural Network, Transformer, Fourier, Dirichlet Kernel, FFT, Boxcar, Pruning, Compression
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TL;DR: A differentiable structured matrix learning framework that can discover new types of structured matrices for efficient DNNs.
Abstract: This paper investigates efficient deep neural networks (DNNs) to replace dense unstructured weight matrices with structured ones that possess desired properties. The challenge arises because the optimal weight matrix structure in popular neural network models is obscure in most cases and may vary from layer to layer even in the same network. Prior structured matrices proposed for efficient DNNs were mostly hand-crafted without a generalized framework to systematically learn them. To address this issue, we propose a generalized and differentiable framework to learn efficient structures of weight matrices by gradient descent. We first define a new class of structured matrices that covers a wide range of structured matrices in the literature by adjusting the structural parameters. Then, the frequency-domain differentiable parameterization scheme based on the Gaussian-Dirichlet kernel is adopted to learn the structural parameters by proximal gradient descent. On the image and language tasks, our method learns efficient DNNs with structured matrices, achieving lower complexity and/or higher performance than prior approaches that employ low-rank, block-sparse, or block-low-rank matrices.
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Primary Area: general machine learning (i.e., none of the above)
Submission Number: 8231