Search Results for author: Andrew Mundy

Found 3 papers, 1 papers with code

Searching for Winograd-aware Quantized Networks

1 code implementation25 Feb 2020 Javier Fernandez-Marques, Paul N. Whatmough, Andrew Mundy, Matthew Mattina

Lightweight architectural designs of Convolutional Neural Networks (CNNs) together with quantization have paved the way for the deployment of demanding computer vision applications on mobile devices.

Neural Architecture Search Quantization

Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs

no code implementations4 Mar 2019 Partha Maji, Andrew Mundy, Ganesh Dasika, Jesse Beu, Matthew Mattina, Robert Mullins

The Winograd or Cook-Toom class of algorithms help to reduce the overall compute complexity of many modern deep convolutional neural networks (CNNs).

Automated Design Space Exploration for optimised Deployment of DNN on Arm Cortex-A CPUs

no code implementations9 Jun 2020 Miguel de Prado, Andrew Mundy, Rabia Saeed, Maurizio Denna, Nuria Pazos, Luca Benini

The framework relies on a Reinforcement Learning search that, combined with a deep learning inference framework, automatically explores the design space and learns an optimised solution that speeds up the performance and reduces the memory on embedded CPU platforms.

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