  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 3x3 tiles which can then be
accelerated with a 3x3 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 5x5 to 9x9 convolutions.
