Funding

Self-funded

Project code

SEM10440526

Start dates

October, February and April

Application deadline

Applications accepted all year round

Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.

TThe PhD will be based in  the School of Electrical and Mechanical Engineering and supervised by Dr Mahsa Mehrad.

The work on this project will involve:

  • Developing AI-based techniques to monitor and optimize the use of FPGA resources.
  • Designing and testing adaptive partial reconfiguration strategies to improve performance and energy efficiency.
  • Building simulation models and implementing experiments on FPGA platforms to validate the proposed methods.
  • Analysing results to identify best practices for AI-driven FPGA optimization in computing systems.

 

 

 

Field-Programmable Gate Arrays (FPGAs) are increasingly critical in modern computing systems because of their ability to perform highly parallel computations while maintaining energy efficiency. They are widely used in applications such as real-time signal processing, edge computing, and embedded devices. However, fully utilizing FPGA capabilities remains challenging. Effective allocation of hardware resources, dynamic task management, and reconfiguration of logic blocks demand specialized knowledge and are time-consuming, limiting the flexibility and scalability of FPGA-based solutions.

This project seeks to integrate artificial intelligence (AI) techniques to enhance the efficiency and adaptability of FPGA-based systems. AI methods will be employed to analyze system workloads, predict resource demands, and optimize task placement across FPGA resources. A major focus will be on partial reconfiguration, which allows sections of an FPGA to be updated dynamically without halting the entire device. 

The research will involve a combination of computational modeling, simulation, and hands-on experimentation. Expected outcomes include a robust framework for AI-guided FPGA resource management and demonstrating significant improvements in performance. 

Through this research, students will gain experience in AI, hardware design, FPGA programming, and system-level optimisation. 

 

 

Fees and funding

Visit the research subject area page for fees and funding information for this project.

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).

 

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.

Entry requirements

You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a master’s degree in Electronic Engineering, Computer Engineering, Computer Science, or a related area. In exceptional cases, we may consider equivalent professional experience and/or Qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

 

  • A strong background in the related field.
  • Experience in digital design, FPGA programming, hardware description languages (HDL) such as VHDL or Verilog is advantageous.
  • Proficiency in programming languages such as Python, C/C++, or MATLAB.
  • Familiarity with machine learning or AI techniques, and programming languages such as Python, C/C++, or MATLAB.

 

 

 

How to apply

We’d encourage you to contact Dr Mahsa Mehrad (mahsa.mehrad@port.ac.uk) to discuss your interest before you apply, quoting the project code.

 

When you are ready to apply, please follow the 'Apply now' link on the Electronic Engineering PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.  Our ‘How to Apply’ page offers further guidance on the PhD application process.

If you want to be considered for this self-funded PhD opportunity you must quote project code SEM10440526 when applying.