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Convolutional neural networks (CNNs) have a large number of variables andhence suffer from a complexity problem for their implementation. Differentmethods and techniques have developed to alleviate the problem of CNN'scomplexity, such as quantization, pruning, etc. Among the differentsimplification methods, computation in the Fourier domain is regarded as a newparadigm for the acceleration of CNNs. Recent studies on Fast Fourier Transform(FFT) based CNN aiming at simplifying the computations required for FFT.However, there is a lot of space for working on the reduction of thecomputational complexity of FFT. In this paper, a new method for CNN processingin the FFT domain is proposed, which is based on input splitting. There areproblems in the computation of FFT using small kernels in situations such asCNN. Splitting can be considered as an effective solution for such issuesaroused by small kernels. Using splitting redundancy, such as overlap-and-add,is reduced and, efficiency is increased. Hardware implementation of theproposed FFT method, as well as different analyses of the complexity, areperformed to demonstrate the proper performance of the proposed method.

Acceleration of Convolutional Neural Network Using FFT-Based Split Convolutions