44 citations · 87 across the 11 of their papers we have counts for
5 papers · 1 filter
Domino Saliency Metrics: Improving Existing Channel Saliency Metrics with Structural Information
Kaveena Persand, Andrew Anderson, David Gregg
Channel pruning is used to reduce the number of weights in a Convolutional Neural Network (CNN). Channel pruning removes slices of the weight tensor so that the convolution layer r…
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…
Composition of Saliency Metrics for Channel Pruning with a Myopic Oracle
Kaveena Persand, Andrew Anderson, David Gregg
The computation and memory needed for Convolutional Neural Network (CNN) inference can be reduced by pruning weights from the trained network. Pruning is guided by a pruning salien…
Low-memory GEMM-based convolution algorithms for deep neural networks
Andrew Anderson, Aravind Vasudevan, Cormac Keane +1
Deep neural networks (DNNs) require very large amounts of computation both for training and for inference when deployed in the field. A common approach to implementing DNNs is to r…
Parallel Multi Channel Convolution using General Matrix Multiplication
Aravind Vasudevan, Andrew Anderson, David Gregg
Convolutional neural networks (CNNs) have emerged as one of the most successful machine learning technologies for image and video processing. The most computationally intensive par…