Flash EQ-Linear: Accelerating Equivariant Linear Layers via Group-wise Discrete Fourier Transform
Equivariant networks embed geometric symmetries as structural priors through weight sharing, achieving remarkable parameter efficiency across vision tasks. However, this parameter efficiency does not translate into compute efficiency: existing implementations unroll the structured weights into dense matrices and dispatch them to generic dense kernels, so the FLOPs of an equivariant layer are no smaller than those of a non-equivariant counterpart. In this paper, we observe that the equivariant linear (EQ-Linear) layer---the most fundamental and frequently used module in modern equivariant archi
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- LinkedLinked via arxiv author · 85%Zhongchen Zhao →
“Flash EQ-Linear: Accelerating Equivariant Linear Layers via Group-wise Discrete Fourier Transform”
- LinkedLinked via arxiv author · 85%Jixin Wang →
“Flash EQ-Linear: Accelerating Equivariant Linear Layers via Group-wise Discrete Fourier Transform”
- LinkedLinked via arxiv author · 85%Qi Xie →
“Flash EQ-Linear: Accelerating Equivariant Linear Layers via Group-wise Discrete Fourier Transform”
- LinkedLinked via arxiv author · 85%Hui Lin →
“Flash EQ-Linear: Accelerating Equivariant Linear Layers via Group-wise Discrete Fourier Transform”