Butterfly Transform: An Efficient FFT Based Neural Architecture Design

CVPR 2020 Keivan Alizadeh VahidAnish PrabhuAli FarhadiMohammad Rastegari

In this paper, we show that extending the butterfly operations from the FFT algorithm to a general Butterfly Transform (BFT) can be beneficial in building an efficient block structure for CNN designs. Pointwise convolutions, which we refer to as channel fusions, are the main computational bottleneck in the state-of-the-art efficient CNNs (e.g. MobileNets )... (read more)

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