Use of Frequency Domain for Complexity Reduction of Convolutional Neural Networks

The implementation of convolutional neural networks (CNNs) is not easy because of the high number of parameters that these networks have. Researchers have applied numerous approaches to reduce the complexity of convolutional networks. Quantization of the weights and pruning are two complexity reduction methods. A new paradigm for accelerating CNNs operations and simplification of the network is to perform all the computations in the Fourier domain. Using a fast Fourier transform (FFT) can simplify the operations by converting the convolution operation into multiplication. Different approaches can be taken for the simplification of computations in FFT. Our approach in this paper is to let the CNN operate in the FFT domain by splitting the input. There are problems in the computation of FFT using small kernels. Splitting is an effective solution for small kernels. The splitting reduces the redundancy that is caused by the overlap-and-add, and hence, the network’s efficiency is increased. Hardware implementation of the proposed FFT method and complexity analysis of the hardware demonstrate the proper performance of the proposed approach.