  The problem how to speed up the convolution computations in Deep Neural
Networks is widely investigated in recent years. The Winograd convolution
algorithm is a common used method that significantly reduces time consumption.
However, it suffers from a problem with numerical accuracy particularly for
lower precisions. In this paper we present the application of base change
technique for quantized Winograd-aware training model. We show that we can
train the $8$ bit quantized network to nearly the same accuracy (up to 0.5%
loss) for tested network (Resnet18) and dataset (CIFAR10) as for quantized
direct convolution with few additional operations in pre/post transformations.
Keeping Hadamard product on $9$ bits allow us to obtain the same accuracy as
for direct convolution.
