PhD Study : AI Hardware: Autonomous Learning for Resilient Edge Computing

Summary

Infrastructure health monitoring of e.g. bridge/building defects is based on edge-computing and face a number of challenges [1]. For example, learning input-output mappings from sensors-to-actuation, and maintaining these mappings in dynamic environments after hardware failures. The edge systems require autonomous learning that scale resources according to task\computing demands, alongside distributed self-monitoring and fine-grained self-repair capabilities. The attributes of autonomy, scalability, robustness, and fault tolerance are widely reported in biological systems. Bio-inspired solutions are increasingly being used to design Artificial Intelligence (AI) systems with the aim of capturing these attributes. The supervisory team at Ulster University has successfully developed bio-inspired approaches for distributed fault-tolerance or self-healing in electronic systems [2-3]. The Computational Neuroscience and Neuromorphic Engineering team at Ulster University have a PhD position available in further addressing the challenges of autonomous learning and fault tolerance for electronic systems.

The PhD position is broad in that the candidate will have an opportunity to engage in fundamental research focused on using our existing algorithms [4] and the development of algorithmic descriptions of how networks of cells oversee learning and self-repair of neural networks. The candidate will have the opportunity to architect these algorithms in hardware using modern FPGAs [5].

This project is strongly interdisciplinary in that the PhD candidate will work with computational modellers (learning) and join an existing team of academics, PhD students and post-doc researchers in this area. The core objectives are defined as:

1. Build upon existing autonomous learning mechanisms developed by the team for self-repaiting neural networks.

2. Develop new building blocks to implement the modified learning mechanism in FPGAs (existing synapse/neuron VHDL blocks available as a starting point [5]).

3. Evaluate the learning algorithm performance (hardware v software).

4. Develop an FPGA hardware demonstrator of a self-repairing neural network, evaluate it and benchmark hardware reliability

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Experience using research methods or other approaches relevant to the subject domain
• A comprehensive and articulate personal statement

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

• Full Award: (full-time tuition fees + £19,000 (tbc))
• Part Award: (full-time tuition fees + £9,500)
• Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living