Energy Predictive Models for Convolutional Neural Networks on Mobile Platforms

no code implementations • 10 Apr 2020 • Crefeda Faviola Rodrigues, Graham Riley, Mikel Lujan

To address this issue, we provide a comprehensive analysis of building regression-based predictive models for deep learning on mobile devices, based on empirical measurements gathered from the SyNERGY framework. Our predictive modelling strategy is based on two types of predictive models used in the literature:individual layers and layer-type.

Navigating the Landscape for Real-time Localisation and Mapping for Robotics and Virtual and Augmented Reality

2 code implementations • 20 Aug 2018 • Sajad Saeedi, Bruno Bodin, Harry Wagstaff, Andy Nisbet, Luigi Nardi, John Mawer, Nicolas Melot, Oscar Palomar, Emanuele Vespa, Tom Spink, Cosmin Gorgovan, Andrew Webb, James Clarkson, Erik Tomusk, Thomas Debrunner, Kuba Kaszyk, Pablo Gonzalez-de-Aledo, Andrey Rodchenko, Graham Riley, Christos Kotselidis, Björn Franke, Michael F. P. O'Boyle, Andrew J. Davison, Paul H. J. Kelly, Mikel Luján, Steve Furber

Visual understanding of 3D environments in real-time, at low power, is a huge computational challenge.

Autonomous Vehicles

Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM

3 code implementations • 8 Oct 2014 • Luigi Nardi, Bruno Bodin, M. Zeeshan Zia, John Mawer, Andy Nisbet, Paul H. J. Kelly, Andrew J. Davison, Mikel Luján, Michael F. P. O'Boyle, Graham Riley, Nigel Topham, Steve Furber

Real-time dense computer vision and SLAM offer great potential for a new level of scene modelling, tracking and real environmental interaction for many types of robot, but their high computational requirements mean that use on mass market embedded platforms is challenging.

Benchmarking