Silicon Laboratories Inc.
Convolutional Neural Networks In The Spectral Domain

Abstract:

A system and method of implementing a convolutional neural network in the spectral domain is disclosed. Rather than performing convolution operations in the spatial domain, the inputs to the convolutional layer and the filter kernels are zero-padded and converted into the spectral domain. Once in the spectral domain, element wise multiplications are performed. The inverse Fourier Transform of the final output is then taken to return to the spatial domain. In certain embodiments, all filter kernels are learned in the spatial domain and are converted to the spectral domain at inference time in the convolutional neural network. In some embodiments a dimensionality reduction operation is applied in the spectral domain. In some embodiments, the conjugate symmetric filter kernels are learned directly in the spectral domain. In other embodiments, the learned spectral kernels apply various forms of dimensionality reduction such as puncturing, low-pass, high-pass, band-pass filtering operations.