  Prior research has shown that Winograd algorithm can reduce the computational
complexity of convolutional neural networks (CNN) with weights and activations
represented in floating point. However it is difficult to apply the scheme to
the inference of low-precision quantized (e.g. INT8) networks. Our work extends
the Winograd algorithm to Residue Number System (RNS). The minimal complexity
convolution is computed precisely over large transformation tile (e.g. 10 x 10
to 16 x 16) of filters and activation patches using the Winograd transformation
and low cost (e.g. 8-bit) arithmetic without degrading the prediction accuracy
of the networks during inference. The arithmetic complexity reduction is up to
7.03x while the performance improvement is up to 2.30x to 4.69x for 3 x 3 and 5
x 5 filters respectively.
