Combining FFT and Spectral-Pooling for Efficient Convolution Neural Network Model
PROCEEDINGS OF THE 2016 2ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INDUSTRIAL ENGINEERING (AIIE 2016)(2016)
Abstract
Convolution operation is the most important and time consuming step in a convolution neural network model. In this work, we analyze the computing complexity of direct convolution and fast-Fourier-transform-based (FFT-based) convolution. We creatively propose CS-unit, which is equivalent to a combination of a convolutional layer and a pooling layer but more effective. Theoretical computing complexity of and some other similar operation is demonstrated, revealing an advantage on computation of CS-unit. Also, practical experiments are also performed and the result shows that CS-unit holds a real superiority on run time.
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Key words
computing complexity,FFT-based convolution,CS-unit
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