Machine Learning (ML) and in particular Deep Learning (DL) require huge numbers of MIPS to implement the training and classification processes required of modern Edge AI applications. All of this comes at a cost of MIPS , power consumption and silicon cost. The frequency domain and, in particular, the Fourier Transform can be used to pre-process the data in many of todays Edge-AI applications to reduce the number of MIPS required for both training and inference. This paper discusses how the frequency domain algorithms can be used and how they are not quite as frightening as they at first appear.
