AI-based Management of FPGA Resources in Computing Systems

About the project

About the Project Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project. The PhD will be based in the School of Electrical and Mechanical Engineering and supervised by Dr Mahsa Mehrad . FPGAs have become a key technology for accelerating computation-intensive tasks, offering unmatched parallelism and energy efficiency. However, designing systems that make full use of FPGA capabilities remains a complex and time-consuming process. This project focuses on using artificial intelligence (AI) to intelligently manage FPGA resources, optimizing performance and adaptability for a wide range of computing applications. The research will explore AI-driven methods to analyze workload requirements, allocate FPGA resources efficiently, and coordinate hardware reconfiguration on demand. By combining simulation and practical experimentation on FPGA platforms, the project aims to create a framework that automatically adjusts hardware configurations in response to changing computational needs. The expected outcomes include improved throughput, reduced energy consumption, and simplified FPGA management, enabling FPGAs to be used more effectively in real-world systems. This approach has potential applications in edge computing, embedded devices, and real-time processing, demonstrating how AI can transform the use of programmable hardware for modern computing challenges. The work on this project will involve: Applying AI methods to optimize FPGA resource allocation for varied computing workloads. Implementing adaptive partial reconfiguration to improve performance and energy efficiency. Validating AI-driven optimization through simulation and experiments on real FPGA platforms. 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. Project description 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 optimization. General admissions criteria 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. International students will require a study visa from UKVI to pursue the degree in the UK. If the research is in a sensitive or technological subject, the student may also need to secure an Academic Technology Approval Scheme (ATAS) certificate from the UK Foreign Office. Specific candidate requirements 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. When applying please quote project code: SEM10440526

Research areas