Winograd Convolution for Deep Neural Networks: Efficient Point Selection

no code implementations • 25 Jan 2022 • Syed Asad Alam, Andrew Anderson, Barbara Barabasz, David Gregg

The choice of points impacts the numeric accuracy of the algorithm, but the optimal set of points for small convolutions remains unknown.

Image Segmentation Object Recognition +1

Quantaized Winograd/Toom-Cook Convolution for DNNs: Beyond Canonical Polynomials Base

no code implementations • 23 Apr 2020 • Barbara Barabasz

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.

Winograd Convolution for DNNs: Beyond linear polynomials

no code implementations • 13 May 2019 • Barbara Barabasz, David Gregg

Winograd convolution is widely used in deep neural networks (DNNs).

Error Analysis and Improving the Accuracy of Winograd Convolution for Deep Neural Networks

no code implementations • 29 Mar 2018 • Barbara Barabasz, Andrew Anderson, Kirk M. Soodhalter, David Gregg

We propose several methods for reducing FP error.