Years 2-4

Building on the summer feasibility study,  years 2-4 are the core research element of the iPhD, during which you continue to work with your supervisors and industry advisor. 

All research projects will be associated with one or more of our themes and teams will take a multidisciplinary approach to challenges associated with them. All projects supported by the Centre will focus on one or more of these themes and will involve academics from across disciplines. Our industry and government partners have active input to project supervision. We emphasise disruptive innovation across disciplinary boundaries, but innovation that is cognizant of its potential impact on society, security and safety.

Research Themes

All research projects will be associated with one or more of our themes and teams will take a multidisciplinary approach to challenges associated with them. All projects supported by the Centre will focus on one or more of these themes and will involve academics from across disciplines. Our industry and government partners have active input to project supervision. We emphasise disruptive innovation across disciplinary boundaries, but innovation that is cognizant of its potential impact on society, security and safety.

Agent Based Adaptive Systems (ABAS)

When we embed AI systems in edge devices, there is an increasing need to enable these systems to work together so that we can develop AI systems at scale; the AI itself needs to work in a decentralised manner. One possible (and natural) way to do so is to build intelligent, autonomous devices (agents) that work together in a self-organised manner and with humans. Projects will investigate novel algorithmic solutions to decentralise machine learning, to enable autonomous systems to coordinate in a secure and trusted way, and to facilitate human-AI collaboration. In addition, we are interested in building new models that emerge through the fusion of novel devices and decentralised and agent-based AI algorithms, and investigate how these could come together to enable complex, resilient systems to be developed at scale respecting local autonomy, and yet providing system-level performance guarantees. Further research challenges include: optimal allocation of tasks across distributed resources under device-specific (e.g. edge versus cloud), geographic, and communications constraints; managing uncertainties over user preferences and demand fluctuations; game-theoretic/incentive models to optimise social welfare; automated and decentralised responses to cyber-attacks; federated learning systems; and decentralised optimisation mechanisms.

Embedded Artificial Intelligence (EAI)

Current state-of-art AI models, particularly machine learning (ML) techniques such as deep neural nets, have very high computational requirements, making them inefficient to embed on devices. With the rise of IoT, however, it is essential to develop new, trusted solutions that can move AI to the network edge. The goal of this theme is to investigate solutions for efficient embedding of AI and ML techniques. In particular, projects may develop energy and memory efficient, yet secure AI/ML algorithms, emphasising holistic design for performance and optimisation. Restricted computational capacity makes edge devices attractive targets of cyber-attacks, and so there are key research questions around the employment of active defences. Similarly, constraints imposed by embedded devices introduce challenges around how to decompose classification, identification and other tasks, to identify what might be done efficiently at the edge, with other, dependent tasks dedicated to cloud infrastructures.

Nanoelectronic Technologies for AI (NTAI)

Computation for AI techniques such as Machine Learning is well known to rely on different fundamental operations than the standard set used for conventional computation. AI-based processing lends itself to an architecture characterised by multiply-accumulate units, on-the-fly adjustable memory and co-located memory and computation. Conventional technologies are not optimised for such tasks, affecting performance, and so we need to explore alternatives at a fundamental materials/device level. MINDS will support research projects in the invention, fabrication, characterisation and initial optimisation of emerging technologies exploiting nanomolecular effects; e.g. the resistive switching effect where we achieve the storage of memory in a highly confined volume and expend just enough energy to move a few atoms in order to reach each new memory state. Projects will develop understanding of the electrochemistry behind these technologies and using this to develop fabrication processes and characterisation techniques. CDT students will, therefore, play a significant role in pioneering novel electronics for AI.

Task-Optimised Devices and Systems (TODS)

The tasks that AI-based models address have varying complexity and real-time processing constraints, requiring flexible hardware acceleration, either locally on user devices or remotely in the cloud. Rather than using separate dedicated hardware for each problem, projects will explore scalable hardware, overlaid with flexible software, combining to support AI algorithms for diverse problems. We will program FPGAs (for cloud computing) and design ASICs (for user devices), to hardware accelerate the most-demanding tasks. Hardware investigated in this theme will focus on scalable solutions at run-time, allowing all resources to be dedicated to solving a single problem, or split to simultaneously solve multiple small problems. Different parts of the hardware may adopt heterogenous designs, optimised for different parts of the processing. Meanwhile, less-demanding processing, specific to different algorithms and problems, may be performed in software, running on CPUs tightly coupled to the hardware. The hardware, software and algorithms will be holistically designed and optimised, to address today’s challenges and allow adaptation for problems that will emerge in the future.

Training and Support

Research at the interface between Artificial Intelligence and Electronics requires us to think differently about the interdependencies between these disciplines, their underpinning mathematical, statistical and algorithmic techniques and device physics. The MINDS CDT brings significant opportunities for students to gain a breadth of knowledge from leading experts.

Cross-Disciplinary Expertise

MINDS students will gain a broad awareness of the science and techniques across AI and Electronics, interdisciplinary approaches to industry challenges, and methods for responsible research and innovation. Depending on your academic background research theme of interest, we tailor a set of additional modules that provide depth of knowledge and understanding from across our extensive portfolio of advanced courses. All students follow a combination of computer science and electronics modules, further developing cross-disciplinary expertise. Modules include deep learning, online and reinforcement learning, intelligent agents, nanoelectronic devices, embedded processors, analogue and mixed signal systems design. This is put into practice through a team project working on a challenge from one of our industry or government partners, the outcomes of which are demonstrated at our annual showcase.

Research project teams are developed over the first 6 months, with MINDS students helping to shape their project and conducting a feasibility study over the summer. Building on the summer feasibility study, years 2-4 are the core research element of the PhD, during which you continue to work with your supervisors and industry advisor. 

Innovation Camp

All MINDS students attend an annual, week-long student-led innovation camp, during which we plan a wide range of activities from enhanced technical training, working with SetSquared to develop skills in research commercialisation, to developing outreach activities. (And social activities, naturally!)

STARs Programme

MINDS students will benefit from a collaborative research network within the CDT allowing them to actively seek other students in related areas and carry out short-term, focussed and hands-on joint research. This may be for the purposes of exploring new ideas, exchanging best-practice, learning how to use new techniques and tools or even simply to gain perspective of different fields and experience multidisciplinary collaborative research. Industrial partners will be included in this network.

Outreach & Public Engagement

All MINDS students will take part in outreach and public engagement activities, helping to both communicate the societal value of research across AI and Electronics and inspire others to pursue a career in this important field for the future. Further, through our Ambassadors’ Programme, we will support a number of students to receive enhanced training in outreach leadership.