14 citations · 25 across the 3 of their papers we have counts for
3 papers
Automated Design Space Exploration for optimised Deployment of DNN on Arm Cortex-A CPUs
Miguel de Prado, Andrew Mundy, Rabia Saeed +3
The spread of deep learning on embedded devices has prompted the development of numerous methods to optimise the deployment of deep neural networks (DNN). Works have mainly focused…
Searching for Winograd-aware Quantized Networks
Javier Fernandez-Marques, Paul N. Whatmough, Andrew Mundy +1
Lightweight architectural designs of Convolutional Neural Networks (CNNs) together with quantization have paved the way for the deployment of demanding computer vision applications…
Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs
Partha Maji, Andrew Mundy, Ganesh Dasika +3
The Winograd or Cook-Toom class of algorithms help to reduce the overall compute complexity of many modern deep convolutional neural networks (CNNs). Although there has been a lot…