PhD Studentship - Training of Deep Learning Networks

Loughborough University

Application details:

Start date: 1st October 2018

Closing date: 9th March 2018


Primary supervisor: David Mulvaney

Secondary supervisor: Vassilios Chouliaras

Intro (standard):

Loughborough University is a top-ten rated university in England for research intensity (REF2014) and an outstanding 66% of the work of Loughborough’s academic staff who were eligible to be submitted to the REF was judged as ‘world-leading’ or ‘internationally excellent’, compared to a national average figure of 43%.

In choosing Loughborough for your research, you’ll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Graduate School, including tailored careers advice, to help you succeed in your research and future career.

Find out more:

Project Detail:

Deep Learning (DL) systems first need to be trained on existing data sets before inference operations can be performed on unseen examples. Most current realisations of (DL) systems target graphical processing units (GPUs), but there has been growing interest in using FPGAs as a hardware platform, since their flexible architecture offers the opportunity of being reconfigured according to the application at hand. A number of FPGA inference solutions have been demonstrated by vendors and researchers, but FPGA training is only just starting to be explored.

This research work will consider alternative approaches for the training of FPGA-based DL systems and compare the performance of these approaches in a small number of applications.

Find out more:

You can find more information about the research currently being carried out by the Electronic Systems Design Group at

Entry requirements:

Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Electronic Engineering, Computer Science, or a related subject. A relevant Master’s degree and/or experience in one or more of the following will be an advantage: Microelectronics, Artificial Intelligence or Machine Learning.

Funding information:

Please note that these studentships will be awarded on a competitive basis to applicants who have applied to this project and/or the following 30 projects that have been prioritised for funding; job advert ref: WS01 – WS32

If awarded, each 3 year studentship will provide a tax-free stipend of £14,786 p.a ( provisional), plus tuition fees at the UK/EU rate (currently £4,262 p.a). While we welcome applications from non EU nationals, please be advised that due to funding restrictions it will only be possible to fund the tuition fees at the international rate and no stipend will be available. Successful candidates will be notified by 30th April 2018.

Contact details:

Name: David Mulvaney

Email address:

Telephone number: 01509 227042

How to apply:

All applications should be made online at Under programme name, select Electronic & Electrical Engineering

Please quote reference number: WS29

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Midlands of England