Optimisation for Efficient Machine Vision
Lead Research Organisation:
University of Oxford
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.
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.
Planned Impact
The UK is faced with an increasing skills shortage, with a recent (2012) large-scale survey reporting that half of all key UK industries surveyed suffer from a worsening skills shortage. This is even more acute in high-tech industry and requires core investment in teaching highly-qualified cohorts, not only the foundational theoretical underpinning in this CDT's remit, but also the acumen to bring this theory to bear on a range of real problems. This CDT will promote training in transformative research that will revolutionise and intertwine theory and practice. If we are to train a generation of researchers to lead in the use of pervasive computation we must actively promote interconnecting research areas. The CDT directly addresses the Autonomous Systems & Robotics priority area and interlinks with priorities in Digitally Connected Citizens, New Digital Ventures, and smart Energy Systems and Digital Healthcare. Furthermore, the CDT has strong links to several current EPSRC challenge themes: 1) Manufacturing the Future: Sustainable manufacturing can only be achieved via autonomy, and machine intelligence at global scale. In today's market, the UK's competitive advantage lies in training highly-skilled researchers that will be able to pioneer distributed autonomous systems into manufacturing processes. 2) Energy: Intelligence and autonomy are key to energy-efficient driving and transportation systems, smart energy grids and efficient use of sparse resources. 3) Digital Economy: Intelligent machines and systems can assist people and give them control over their lives in a number of contexts, such as assisted living, home healthcare, transportation, skill & knowledge transfer and telepresence. 4) Living with Environmental Change: Intelligent hand-held devices and participatory sensing will extend environmental monitoring to unprecedented spatial and temporal scales, building real sensor systems and citizen science platforms to monitor the environment, pollutants and biodiversity.
The CDT will allow us to bring together our collaborations with industrial partners into a unique consortium, which will underpin the student training program, from fundamentals to development, deployment and use. The CDT has secured support not only from the University, but also from a team of industrial partners, who share our vision. We have support from an impressive list of companies, from global multi-nationals and large corporations, such as BAE Systems, BP, Schlumberger & YouGov (internships, studentships and membership of the external steering group), Microsoft, Google, Honeywell, Ascending Technologies, SciSys & Man Group (internships & part of our external steering group), ABB, Infosys, QinetiQ (internships and studentships). Industry and commerce will have an active participation in the CDT programme via internships and studentships; provision of short lectures highlighting the practical application of the taught material; proposing first-year research projects; membership of the steering committee; industrial placements into Oxford. Industrial participation, at all levels, will enhance the quality of the training programme and provide access to a unique pool of CDT talent. We believe that our approach to industrial engagement places realistic requirements on both industry and students.
The benefits of the CDT will be many-fold. The students will benefit via a strong foundation in the principles & practice of autonomous & intelligent systems and subsequent research with world-leading groups. The enthusiasm shown by a range of industries indicates an appetite for engaging with the student cohort, promoting clear dissemination, impact and collaboration routes benefiting industry, academia and the UK economy.
The CDT will allow us to bring together our collaborations with industrial partners into a unique consortium, which will underpin the student training program, from fundamentals to development, deployment and use. The CDT has secured support not only from the University, but also from a team of industrial partners, who share our vision. We have support from an impressive list of companies, from global multi-nationals and large corporations, such as BAE Systems, BP, Schlumberger & YouGov (internships, studentships and membership of the external steering group), Microsoft, Google, Honeywell, Ascending Technologies, SciSys & Man Group (internships & part of our external steering group), ABB, Infosys, QinetiQ (internships and studentships). Industry and commerce will have an active participation in the CDT programme via internships and studentships; provision of short lectures highlighting the practical application of the taught material; proposing first-year research projects; membership of the steering committee; industrial placements into Oxford. Industrial participation, at all levels, will enhance the quality of the training programme and provide access to a unique pool of CDT talent. We believe that our approach to industrial engagement places realistic requirements on both industry and students.
The benefits of the CDT will be many-fold. The students will benefit via a strong foundation in the principles & practice of autonomous & intelligent systems and subsequent research with world-leading groups. The enthusiasm shown by a range of industries indicates an appetite for engaging with the student cohort, promoting clear dissemination, impact and collaboration routes benefiting industry, academia and the UK economy.