Acceleration of Convolutional Neural Network Using FFT-Based Split Convolutions

Understand

Convolutional neural networks (CNNs) have a large number of variables and hence suffer from a complexity problem for their implementation.

• Different methods and techniques have developed to alleviate the problem of CNN's complexity, such as quantization, pruning, etc.
• Among the different simplification methods, computation in the Fourier domain is regarded as a new paradigm for the acceleration of CNNs.
• Recent studies on Fast Fourier Transform (FFT) based CNN aiming at simplifying the computations required for FFT.