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Deformable Butterfly: A Highly Structured and Sparse Linear Transform

2022-03-25 10:20:50
Rui Lin, Jie Ran, King Hung Chiu, Graziano Chesi, Ngai Wong

Abstract

We introduce a new kind of linear transform named Deformable Butterfly (DeBut) that generalizes the conventional butterfly matrices and can be adapted to various input-output dimensions. It inherits the fine-to-coarse-grained learnable hierarchy of traditional butterflies and when deployed to neural networks, the prominent structures and sparsity in a DeBut layer constitutes a new way for network compression. We apply DeBut as a drop-in replacement of standard fully connected and convolutional layers, and demonstrate its superiority in homogenizing a neural network and rendering it favorable properties such as light weight and low inference complexity, without compromising accuracy. The natural complexity-accuracy tradeoff arising from the myriad deformations of a DeBut layer also opens up new rooms for analytical and practical research. The codes and Appendix are publicly available at: this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2203.13556

PDF

https://arxiv.org/pdf/2203.13556.pdf


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