44 citations · 87 across the 11 of their papers we have counts for
12 papers
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…
TASO: Time and Space Optimization for Memory-Constrained DNN Inference
Yuan Wen, Andrew Anderson, Valentin Radu +2
Convolutional neural networks (CNNs) are used in many embedded applications, from industrial robotics and automation systems to biometric identification on mobile devices. State-of…
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…
Performance-Oriented Neural Architecture Search
Andrew Anderson, Jing Su, Rozenn Dahyot +1
Hardware-Software Co-Design is a highly successful strategy for improving performance of domain-specific computing systems. We argue for the application of the same methodology to…
Taxonomy of Saliency Metrics for Channel Pruning
Kaveena Persand, Andrew Anderson, David Gregg
Pruning unimportant parameters can allow deep neural networks (DNNs) to reduce their heavy computation and memory requirements. A saliency metric estimates which parameters can be…
Scalar Arithmetic Multiple Data: Customizable Precision for Deep Neural Networks
Andrew Anderson, David Gregg
Quantization of weights and activations in Deep Neural Networks (DNNs) is a powerful technique for network compression, and has enjoyed significant attention and success. However,…
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…
Optimal DNN Primitive Selection with Partitioned Boolean Quadratic Programming
Andrew Anderson, David Gregg
Deep Neural Networks (DNNs) require very large amounts of computation both for training and for inference when deployed in the field. Many different algorithms have been proposed t…
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…
Vectorization of Multibyte Floating Point Data Formats
Andrew Anderson, David Gregg
We propose a scheme for reduced-precision representation of floating point data on a continuum between IEEE-754 floating point types. Our scheme enables the use of lower precision…