Research projects

AI Hub for Productive Research & Innovation in Electronics

Our national consortium funded by UKRI comprises 20 academic institutions and over 30 industrial partners that brings to market AI-based tools to boost productivity across the electronics industry supply chain. APRIL is transforming the electronics industry by developing world-leading AI research relevant to the electronics industry, building a strong expandable network that can influence the direction of research, policy and regulation, enabling the adoption of technology into business through the creation of AI tools or translation into new start-ups and generating a strong pipeline of talent thatspans AI and electronics. The APRIL Hub will lead the way in AI research, focusing on key areas of the electronics supply chain. APRIL’s development of AI tools will lead to faster, cheaper, greener and overall more power-efficient electronics.

Adiabatic Capacitive Artificial Neuron

Bigger and more capable AI systems require increasingly more power to operate at full capacity, straining global energy supplies and limiting the democratisation of AI. Project ACAN, funded by DSTL, uses charge recovery to implement AI systems with far higher power efficiency; aiming to lower the power consumption of such systems by a factor of 10x while remaining fully compatible with current, tried and tested CMOS manufacturing technology. The approach is particularly promising when applied on neural networks, where it leverages their parallelism. Our aim is to demonstrate how modular ACAN-type artificial neurons can be implemented and combined together into practically usable networks of virtually any shape and size. In areas where both cost and power budgets are severely constrained,  applications in low-power, long-lasting implants for medical use and microchips for autonomous drones will become critical. 

Functional-oxide Reconfigurable Technologies

The EPSRC-funded programme grant FORTE brings together world-leading expertise across the University of Edinburgh, Imperial College London and the University of Manchester with the ambition to transform ICT through delivering highly scalable, resilient, power efficient and affordable reconfigurable electronic systems. Our approach is targeted on rebalancing the UK electronics remit from a ‘more’ to ‘beyond Moore’ era via developing functional oxide-based memristor-based architectures and integrating these with CMOS for boosting conventional electronics capabilities. This will allow our society to efficiently expand the operational envelope of electronics, enabling the use of electronics in inaccessible environments as well as reusing or re-purposing electronics affordably.

Shiwei Wang's Bayes Innovation Fellowship focuses on innovating novel chip technologies that interface with the brain. A short-term target is to change the limitation that no technologies today allow reliable neural recording and stimulation in a concurrent manner. Testing and fine-tuning of devices intended to treat or diagnose neurological conditions are difficult without knowing the brain’s immediate responses to stimuli. Shiwei’s team has tackled this challenge using a novel integrated circuit chip that can readout tiny neural signals with high fidelity even in the presence of large stimulation artifacts. The technology has been validated in-vitro and in-vivo, and he is translating this innovation into commercial products. The immediate impact is to allow more reliable data from pre-clinical testing, accelerating neurological healthcare innovation.

AI MeTLLE - Memristive Technologies for Lifelong Learning Embedded AI Hardware

The Royal Academy of Engineering’s Chair in Emerging Technologies scheme aims to identify global research visionaries and provide them with long term support to lead on developing emerging technology areas with high potential to deliver economic and social benefit to the UK. In 2020 Themis Prodromakiswas awarded a Chair in Emerging Technologies for his work on Memristive Technologies for Lifelong Learning Embedded AI Hardware, using innovations in nanotechnology to create a new electronic fabric that merges memory with computing power while maintaining extreme power efficiency – as the human brain does. The research, using memristors or memory chips based on transition metal-oxides, focuses on enabling electronic systems to sense, recognise, learn and reason, with the goal of embedding artificial intelligence.

Artificial intelligence Enabled Guide for Investment Strategies

AI is a rapidly evolving and highly disruptive technology being implemented within the financial services industry. The risks associated with implementing AI in this field must be adequately managed. Project AEGIS uses generative AI to carry out trading or insuring activity in a high-quality, trustworthy and self-correcting manner. We are developing software that can perform important trading/insuring tasks autonomously, and can be equipped with a rudimentary set of circuit-breakers. This will be accompanied by a set of benchmark tests illustrating how the software can be expected to perform under various market conditions whilst outlining the challenge/opportunity space. Thus, AEGIS seeks to set the foundations for what we fully expect will become an ever more elaborate system, eventually “earning the trust” to work side-by-side with a human in the financial industry.

This joint Energy Technologies Institute (ETI) and EPSRC fellowship award is held by Julianna Panidi whose focus is on developing methods for improving the sustainability of solution-processed solar cells and using them as light-powering sources. Attention is given to the material selection and processing methods for the development of organic solar cells without compromising their power conversion efficiency. Another key area for Julianna’s focus is their stability, which will be evaluated in indoor and outdoor settings. By combining electrical and morphological characterisation, Julianna aims to understand the main degradation mechanisms, which will then support future developments.

Low-Power, High-Speed, Adaptable Processing-In-Sensing Capability

ProSensing defines a novel approach to embed intelligence locally, enabling training at the edge by developing novel in-sensing processing elements that will be combined with tinyML technologies. The development of an in-sensor processing architecture using emerging devices (RRAMs) for image classification is proposed, with the capability of being used in various domains.  We aim to eliminate the analogue-to-digital interfacing complexity by processing the extracted features as analogue vectors. ProSensing is a collaborative programme with the Universities of Strathclyde and Nottingham Trent.

Doctoral Training Centre in Sensing, Processing, and AI for Defence and Security

This University of Edinburgh led EPSRC and MoD Centre for Doctoral Training Programme focuses on generation-after-next technologies for information processing in defence and security, from hardware development to algorithmic AI development. SPADS is training the next generation of defence scientists, capable of leading developments to transform defence, security, and broader civilian society. The approach will underpin interconnected technology and sensing modalities working across multiple sensing domains.  Alex Serb is a SPADS Co-Director,  leading on Novel computing and Beyond-CMOS hardware.