PhD Study : Cardio-AI-ReAccel: Reconfigurable Accelerators for Artificial Intelligence in Cardiology

Summary

This project is targeted towards the development of hardware accelerators for reduction in execution time of complex Artificial Intelligence (AI) algorithms in cardiology. AI has had a profound impact on our lives. Cardiac healthcare has highly benefited from the advances in AI and machine learning. AI in cardiology aids in identification of novel drug therapies, disease statistics, continuous remote monitoring and diagnostics, integration of multi-omic data, personalised medicine and in increasing physician efficiency. With the proliferation of wearable and mobile sensors, ubiquitous sensing has become a reality, leading a generation of large amounts of digital cardiac data.

AI has helped in detecting cardiovascular diseases like arrhythmias, left ventricular systolic dysfunction and congestive heart failure. Machine learning typically consists of two types of algorithms: Training and Inferencing. Machine learning training algorithms are very complex and are typically run on the cloud due to high computational demands. Neural networks have become one the most popularly used machine learning algorithms. In order to train these neural networks, lots of computation (several giga floating point operations) is required. These computations take a very long time typically ranging from days to weeks. Cloud service providers are adopting accelerators to satisfy computational demands and reduce the execution time of these complex algorithms (N. Normanet al. 2017).

Amazon Cloud provides multiple Field Programmable Gate Array (FPGAs) and multiple Graphics Processing Units (GPUs) to its users. Application Specific Integrated Circuit (ASICs) show 2x to 3x performance improvements in speed compared to other hardware accelerators like GPUs and FPGAs and up to 190x performance/watt when compared to CPUs. Microsoft executes many of their machine learning applications on FPGA  Azure Cloud. Google’s Tensor Processing Units (TPUs) for accelerating machine learning applications is an ASIC. Inference is carried out on the edge (point of care) devices to identify any abnormalities/conditions where the main challenge is energy efficiency and training on the edge. Advancing the state-of-the-art in cardiac healthcare is highly dependent on reducing the execution time and interpreting results at real-time which is possible by development of novel reconfigurable accelerators.

The PhD Researcher will research development of reconfigurable hardware (FPGA) accelerators for reducing the computation time of cardiac AI algorithms. The Researcher will profile the software using profiling tools to identify parts (kernels) of the software which are taking up significant execution time. These Kernels will be implemented in hardware on FPGAs. The student will investigate modification of arithmetic precision of computations in order to get faster results with minimal loss of accuracy. However, precision reduction will be made tuneable so that users can choose between accuracy vs speed.

The Researcher will also have opportunities to collaborate and intern with researchers at Xilinx, Queens University Belfast and The University of Hong Kong.

This PhD project will be composed of challenging and timely research as well as bespoke training and mentoring that will equip the Researcher with technical and research skills needed to advance his/her career either in industry or academia.

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.

• Sound understanding of subject area as evidenced by a comprehensive research proposal

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 65%
• Work experience relevant to the proposed project
• Publications - peer-reviewed

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