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Department of Computer Science

Hardware Aware Training for AI Systems

Primary supervisor

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Other projects with the same supervisor


  • Competition Funded Project (Students Worldwide)

This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.

Project description

Neural network based AI systems have made remarkable progress over the last decade, with applications in computer vision, language processing and robotics. As performance has increased, so has the data required to train these systems, along with the size of the underlying models. This has resulted in growing energy demands, both during training and in deployment.

Solutions to the energy challenges of neural network systems are being developed in the form of new hardware, including advances in traditional massively parallel systems such as GPUs, but also with novel approaches such as memristor based systems and neuromorphic hardware. These novel systems provide orders of magnitude reductions in power, offering game-changing performance both in data centres and mobile edge devices. However, these systems often present challenges when implementing conventional neural network training methods, as operations represented in software are not accurately or reliably implemented in hardware. This makes exploiting the energy potential of these systems difficult.

The goal of this project is to explore hardware-aware training methods for AI systems. Developing algorithms and representations which take into account the underlying characteristics of the hardware during training, to deliver low-power hardware implementations which are accurate, reliable and robust.

Person specification

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Applicants will be required to evidence the following skills and qualifications.

  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.


Applicants will be required to evidence the following skills and qualifications.

  • You will have good time management.
  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.


Applicants will be required to address the following.

  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Why do you believe you are suitable for doing Postgraduate Research?