Autonomous Electronic Systems


Adapt or perish, now as ever, is nature’s inexorable imperative – H. G. Wells

Energy efficiency and autonomy are two crucial requirements for the current and future generations of electronic systems. These are key to enabling pervasive applications that will perpetually work to make our interactions with the environment better and smarter for better living and well-being. However, ensuring energy efficiency and autonomy at low-cost is highly challenging.

In this lab, we investigate into modeling, prototyping and implementation of low-cost hardware and system-level methodologies to tackle the above challenges. The overall aim is to learn how applications, such as artificial intelligence, exercise the underlying hardware architecture and investigate opportunities to: 1) prune the circuit complexity in favour of energy reduction, 2) control the power/performance knobs autonomously to ensure energy-efficiency, and 3) develop new architectures that are by-design energy-efficient, autonomous and reliable.

A wordmap of the keywords relevant to our research is shown below:

Specifically, our current research interests are:

A. Learning Automata driven AI Hardware Design: We are pioneering a brand new AI hardware design method using the principle of learning automata. The new AI solution is built on propositional logic and game theory, and as such has inherent energy-frugality and explainability features.

B. Energy-efficient and Reliable Electronic Systems Design: using hardware/software co-design and co-optimisation; programming models (OpenMP, OpenCL, OpenCV) and middleware/firmware designs.

C. Approximate Computing Systems Design: using variable exponent arithmetic and adaptive programming models for large scale systems, such as big data applications, and cloud server systems.

D. Software/Hardware Co-design for Emerging/Translational Applications: Custom hardware and firmware design of translational applications and deep-learning systems with the aim of achieving required energy efficiency and performance.

If you are interested in the research and development of the above or similar areas, and would like to get involved as a new postgraduate (i.e. PhD) researcher or intern, please contact Dr. Rishad Shafik (e-mail: rishad <dot> shafik <at> Newcastle <dot> ac <dot> uk) with your CV and research statement.

More details regarding Dr. Rishad Shafik can be found here.