PhD Studentship: Enhancing AI Performance with AMD's Neural Processing Units

DoS: Dr Vasilios Kelefouras (v.kelefouras@plymouth.ac.uk)

2^nd Supervisor: Dr Vassilis Cutsuridis (vassilis.cutsuridis@plymouth.ac.uk)

3^rd Supervisor: Dr Krithika Anil (krithika.anil@plymouth.ac.uk )

Industrial Partner:

This project is partly funded by Advanced Micro Devices (AMD). AMD is a leading global semiconductor manufacturer and technology company.

Applications are invited for a 3.5-year PhD studentship. The studentship will start on 1^st October 2025.

Project Description

As Deep Learning and Generative AI applications continue to expand, optimizing computational efficiency is becoming increasingly critical, particularly for AI in resource-constrained environments or at the edge.

To address this challenge, semiconductor manufacturers have introduced dedicated Neural Processing Units (NPUs), significantly enhancing performance and energy efficiency. AMD, for example, integrates a CPU, NPU, and GPU into its latest Ryzen processors, unlocking new possibilities for on-device AI acceleration.

This project aims to maximize the potential of AMD’s cutting-edge NPUs in the area of computer vision. This project is partially funded by AMD and the successful candidate will collaborate with AMD researchers.

As part of this research, you will:

• Investigate how different AI tasks perform on AMD Ryzen CPU, GPU, and NPU, in terms of inference speed, energy consumption, and performance per watt. Different quantization levels (e.g., int8, fp16) will also be explored.
• Develop intelligent workload allocation techniques to maximize resource utilization.
• Develop optimized software routines of commonly used AI tasks tailored for AMD NPUs.
• Validate these methods on two real-world applications: bone fracture recognition and video-based sign language recognition.

Why Apply?

• Conduct high-impact research in AI system acceleration.
• Collaborate with AMD engineers, gaining valuable industry experience.
• Benefit from strong career prospects in both academia and industry.

Candidate Requirements:

We seek highly motivated candidates with expertise in programming (C++, Python), computer architectures, Deep Learning (PyTorch, TensorFlow), and AI acceleration.

Exceptional international candidates may be eligible for a fee waiver (read the following section carefully).

Eligibility

Applicants should have a first or upper second class honours degree in an appropriate subject and preferably a relevant Masters qualification. Applications from both UK and overseas students are welcome.

The studentship is supported for 3.5 years and includes full Home tuition fees, Bench fee plus a stipend of £20,780 per annum 2025/26 rate.  The studentship will only fully fund those applicants who are eligible for Home fees with relevant qualifications.  Applicants normally required to cover International fees will have to cover the difference between the Home and the International tuition fee rates.  The international component of the fee may be waived for outstanding international applicants.

There is no additional funding available to cover NHS Immigration Health Surcharge (IHS) costs, visa costs, flights etc.

NB: The studentship is supported for 3.5 years of the four-year registration period.

The subsequent 6 months of registration is a self-funded ‘writing-up’ period.

If you wish to discuss this project further informally, please contact:

Dr Vasilios Kelefouras v.kelefouras@plymouth.ac.uk .

To apply for this position please click the Apply button above.

Please clearly state the name of the studentship that you are applying for on your personal statement.

Please see here for a list of supporting documents to upload with your application.

The closing date for applications is 12 noon on 28^th March 2025.

Shortlisted candidates will be invited for interview shortly thereafter.

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