Deformable Butterfly: A Highly Structured and Sparse Linear Transform

1 code implementation • NeurIPS 2021 • Rui Lin, Jie Ran, King Hung Chiu, Graziano Chesi, Ngai Wong

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.

Exploiting Elasticity in Tensor Ranks for Compressing Neural Networks

no code implementations • 10 May 2021 • Jie Ran, Rui Lin, Hayden K. H. So, Graziano Chesi, Ngai Wong

Elasticities in depth, width, kernel size and resolution have been explored in compressing deep neural networks (DNNs).

HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression

no code implementations • 28 Feb 2020 • Rui Lin, Ching-Yun Ko, Zhuolun He, Cong Chen, Yuan Cheng, Hao Yu, Graziano Chesi, Ngai Wong

The emerging edge computing has promoted immense interests in compacting a neural network without sacrificing much accuracy.

Edge-computing Tensor Decomposition