Optimisation for Efficient Machine Vision

Lead Research Organisation: University of Oxford
Department Name: Autonom Intelligent Machines & Syst CDT

Abstract

This research is closely aligned with EPSRC research areas of Artificial Intelligence, Image and vision computing.

Aims and Objectives:

Machine Vision has undergone rapid development during the last 6 years with the state of the art on a range of benchmarks being persistently improved by new machine vision techniques. Many of these recent techniques in machine vision leverage large convolutional neural networks (CNNs) that require graphics processing units (GPUs) to both train and run at inference time because of their large computational load. However, the power, cost and space requirements of GPUs prohibits the applications of these techniques in many settings.

This research aims to develop novel machine vision methods, with a focus on efficient operation. As a starting point this research will look to develop novel methods for training Binary and Quantised Neural networks by using discrete programming relaxations to train binary neural networks.

If comparable results to modern CCNs could be replicated on low powered CPUs such as those found in mobile devices this would have a huge impact on the areas of self-driving cars, robotics, smart data acquisition and portable AI.

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/R512333/1 01/10/2017 30/09/2021
1906386 Studentship EP/R512333/1 01/10/2017 30/09/2021 Alasdair Paren