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PhD in Algorithmic Approximate Computing


Project Description

Project description:

The aim of this PhD project is to develop techniques for performing approximate computation in energy-constrained environments. Although battery lifetime has increased significantly over the last years, the most sophisticated data processing algorithms require so much computation that they cannot be deployed in operations using battery-powered drones or sensor networks. Such massive computation would indeed compromise the longevity of the operation. A solution is then to approximate the algorithms by decreasing the amount of computation, which can be done at the algorithmic level (e.g., by the selection of a more efficient optimization algorithm), all the way down to the hardware level (e.g., by reducing voltage).

This project will focus on the approximation aspects at the higher-levels, including the consideration of approximation at the problem formulation, the design of resilient optimization algorithms, and the development of approximate linear algebra. The goal is not only to design approximation algorithms, but also to analyze how approximations introduce errors and how these, in turn, affect the algorithm’s performance and power savings. The student will look at two classes of algorithms for imaging tasks, such as classification, reconstruction, or super-resolution. One class of algorithms is based on convex optimization principles, for which there exist comprehensive performance analyses. The other class is based on nonconvex optimization, including algorithms used to train/update neural networks, for which performance characterizations are quite limited.

This project will be supervised by Dr. Joao Mota, Prof. Mathini Sellathurai, and Prof. Andrew Wallace, and will take place at Heriot-Watt University, Edinburgh.

The student will work on the University Defence Research Collaboration (UDRC) (www.mod-udrc.org), which is a leading research partnership for signal processing for defence and develops new techniques to better transform data across many domains into actionable information, and meet the requirements for improved situational awareness, information superiority, and autonomy. This collaboration, sponsored by Dstl and the EPSRC, is academia-led and has commenced its third phase of research focusing on "Signal Processing in the Information Age". The Consortium is made up of the University of Edinburgh, Heriot-Watt University, Queen’s University Belfast and University of Strathclyde and there are currently PhD opportunities available across the four universities to work on diverse topics in signal processing, as part of a collaborative team. The work will involve strong links with industry and the UK defence sector. The PhD student will be expected to work closely with other research team members and to attend regular meetings to present project updates to the sponsors and partners of this project.

Candidates should have completed, or expect to complete, an MSc degree in Electrical Engineering, Mathematics, Computer Science, or similar. A strong mathematical background is essential and knowledge of optimisation algorithms, linear algebra, algorithm analysis, or machine learning is a big plus. Candidates should have an autonomous and proactive working style, and good communication skills.

Funding Notes

The studentship is available for 3 years, starting from September/October 2019. There will be an annual stipend of around £15k, and no nationality restrictions.

References

To apply, please send a CV, a cover letter, and contact details of at least two referees to Dr. Joao Mota ([email protected]) and to Prof. Mathini Sellathurai ([email protected]), quoting "PhD in Algorithmic Approximate Computing" in the email subject. Informal queries can also be addressed to Dr. Mota

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