44 citations · 47 across the 3 of their papers we have counts for
4 papers
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…
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…
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,…
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…