The anatomy of efficient FFT and winograd convolutions on modern CPUs

Abstract

Winograd-based convolution has quickly gained traction as a preferred approach to implement convolutional neural networks (ConvNet) on various hardware platforms because it could require fewer floating point operations than FFT-based or direct convolutions.

In this paper, we analyze the theoretical performances of three methods (regular FFT-, Gauss-FFT-, and Winograd-based convolutions), as well as compare their highly optimized implementations on modern multi- and many-core CPUs. With all three implementations employing the same optimizations on modern CPUs, our experimental results with modern ConvNets show that the FFT-based implementations generally outperform the Winograd-based approach, which is contrary to the popular belief.

To understand the results, we use a Roofline performance model to analyze the three implementations in detail, by looking at each of their computation phases and by considering not only the number of floating point operations, but also the memory bandwidth and the cache sizes. The performance analysis explains why, and under what conditions, the FFT-based implementations outperform the Winograd-based one, on modern CPUs.