Journal:Under Review (arXiv:2102.13272)

Key Words:Convolutional Neural Network (CNN), Winograd CNN, nesting decomposition algorithm

Abstract:Recent literature found that convolutional neural networks (CNN) with large filters perform well in some applications such as image semantic segmentation. Winograd transformation helps to reduce the number of multiplications in a convolution but suffers from numerical instability when the convolution filter size gets large. This work proposes a nested Winograd algorithm to iteratively decompose a large filter into a sequence of 3×3 tiles which can then be accelerated with a 3×3 Winograd algorithm. Compared with the state-of-art OLA-Winograd algorithm, the proposed algorithm reduces the multiplications by 1.41 to 3.29 times for computing 5×5 to 9×9 convolutions.

Co-author:Xizi Chen,Chi-Ying Tsui

First Author:Jingbo Jiang

Translation or Not:no