2 citations · 4 across the 3 of their papers we have counts for
4 papers
Winograd Convolution for Deep Neural Networks: Efficient Point Selection
Syed Asad Alam, Andrew Anderson, Barbara Barabasz +1
Convolutional neural networks (CNNs) have dramatically improved the accuracy of tasks such as object recognition, image segmentation and interactive speech systems. CNNs require la…
Quantaized Winograd/Toom-Cook Convolution for DNNs: Beyond Canonical Polynomials Base
Barbara Barabasz
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…
Winograd Convolution for DNNs: Beyond linear polynomials
Barbara Barabasz, David Gregg
Winograd convolution is widely used in deep neural networks (DNNs). Existing work for DNNs considers only the subset Winograd algorithms that are equivalent to Toom-Cook convolutio…
Error Analysis and Improving the Accuracy of Winograd Convolution for Deep Neural Networks
Barbara Barabasz, Andrew Anderson, Kirk M. Soodhalter +1
Popular deep neural networks (DNNs) spend the majority of their execution time computing convolutions. The Winograd family of algorithms can greatly reduce the number of arithmetic…