New UK research network to advance low-cost, low-energy AI

The Nottingham Trent University-led ‘TinyML UK Network’ brings together experts across AI, electronics, hardware design and embedded systems to establish collaboration and shape future research priorities in this key area.

While powerful, AI systems today depend on large models, centralised cloud infrastructure and continuous data transfer.

They are costly, energy-intensive and increasingly difficult to keep sustainable – and there are also concerns around privacy, resilience and digital sovereignty.

Decentralised AI and tiny machine learning (TinyML) however allows AI to run directly on small low-power devices rather than relying on large data centres.

AI can operate locally, respond in real time and continue functioning even when connectivity is limited, by running machine learning directly on sensors, wearables and embedded systems.

The new network is being funded by UK Research and Innovation through the Engineering and Physical Sciences Research Council, with the University of Southampton and Imperial College London as co-leads.

It will act as a UK-focused hub, linking researchers with industry to accelerate innovation, support skills development and ensure that future AI systems are efficient, trustworthy and decentralised by design.

The network argues that recent technological advances have made it feasible to begin to deploy meaningful AI on tiny, affordable and energy-efficient devices.

They emphasise a move away from ever-larger models towards specialist, smaller models that are adaptive, autonomous and capable of coordination. These models form distributed AI systems, whereby intelligence emerges from collaboration across many devices rather than from a single large model.

This shift requires new approaches as to how models are trained, deployed, updated and coordinated across networks of hardware.

It keeps data close to where it is generated, reducing latency and energy use while improving privacy and reliability.

It also enables AI to be deployed at scale in real-world environments such as homes, cities, farms, hospitals and natural landscapes.

TinyML is already making a real-world difference across a range of applications. Examples include models embedded in livestock devices which are able to learn behavioural patterns to identify health anomalies in animals, and personal safety devices which detect abnormal motion or acoustic patterns on-device and trigger alerts without storing recordings.

“AI adoption is accelerating, alongside concerns over energy consumption, infrastructure cost, resilience, privacy and sustainability,” said network lead Eiman Kanjo, Professor of Pervasive Sensing and TinyML in Nottingham Trent University’s School of Science and Technology and Honorary Provost Visiting Professor at Imperial College London.

She said: “This is our opportunity to bring together our engineering, electronics and AI communities to build decentralised, low-energy, privacy-preserving and affordable systems.

“The new TinyML UK Network is our chance to grow UK capability and help lead in this space.

“The network will connect AI, hardware, embedded systems and engineering researchers across the UK. It will build strong links with international industry and global TinyML leaders, run training, competitions and events for students, researchers and SMEs, support real-world impact in health, sustainability and security, and help shape a UK roadmap for future TinyML research and skills.”