Training of Deep Learning Networks PhD

Department(s):
Mechanical, Electrical and Manufacturing Engineering
Entry requirements:
2:1+
Full-time:
3 years
Part-time:
not available
Reference number:
WS29
Start date:
01 October 2018
Is funding available?
Yes
UK/EU fees:
N/A
International fees:
TBC
Location:
Loughborough
Application deadline:
09 March 2018

Achievements

3rd

in the UK for research quality

REF 2014

10th

in the UK for Mechanical Engineering

The Complete University Guide 2018

Winner

of 2 Queen's Anniversary Prizes

Overview

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 Doctoral College, including tailored careers advice, to help you succeed in your research and future career. 

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.

Supervisors

Primary supervisor: David Mulvaney

Secondary supervisor: Vassilios Chouliaras

Find out more

For further project details email David Mulvaney or register your interest and ask us a question.

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

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.

Applicants must meet the minimum English Language requirements, details available on the website.

Fees and funding

UK/EU:
N/A
International:
TBC

Tuition fees cover the cost of your teaching, assessment and operating University facilities such as the library, IT equipment and other support services. University fees and charges can be paid in advance and there are several methods of payment, including online payments and payment by instalment. Special arrangements are made for payments by part-time students.

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.

How to apply

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

Please quote reference number: WS29

Application details

Reference number:  WS29
Start date: 01 October 2018
Application deadline: 09 March 2018

Explore