Thinking like a machine: how Manchester is shaping the next era of brain-inspired computing

Researchers at the University of Manchester are advancing neuromorphic computing, a brain-inspired approach that could make AI and sensing more energy-efficient. The SpiNNaker platform, the world's largest neuromorphic system, models spiking neural networks at the scale of a mouse brain, while new projects explore practical applications in edge computing and wearable health monitors.

Thinking like a machine: how Manchester is shaping the next era of brain-inspired computing Manchester’s legacy of rethinking computing continues through neuromorphic systems, where researchers are exploring brain-inspired technologies to make AI and sensing more efficient. In 1951, engineers at The University of Manchester and Ferranti Ltd. reframed how they thought about computers, approaching the problem of stored memory in a new and revolutionary way. Their research and innovative collaboration resulted in the Ferranti Mark I which helped move computing from the laboratory into the wider world. Since then, computer engineers have been able to rely on a simple assumption: the next generation of chips would be faster, cheaper and more powerful than the last. But as we reach the limits of conventional computing, some researchers have started asking a more fundamental question. What if the problem is not how quickly computers process information, but how they process it in the first place? Building a brain Biological brains are among the most energy-efficient information-processing systems known; running on about 20 watts of power – barely enough to power a dim LED light bulb – and only “spiking” when needed, they can process vast amounts of information very quickly. It is perhaps unsurprising that researchers have looked to biology for inspiration. Now, a previously niche area of research is coming to the fore; neuromorphic literally, “brain-like” computing is a way of designing computer systems that use technologies such as spiking neural networks, event-driven sensors and specialised low-power hardware to process information more efficiently and in ways that more closely resemble natural intelligence. Neuro-: derived from neuron, meaning “nerve”, “tendon”, or “cord” -morphic: derived from morphē, meaning “form”, “shape”, or “structure” Spiking neural network: a computer model inspired by how neurons in the brain communicate. Instead of processing information as a steady stream of numbers, its “neurons” send short bursts of signals – or spikes – only when needed. With the rapid proliferation of AI and the associated environmental challenges facing the data centres that support it , and the limitations of current computer hardware, researchers exploring neuromorphic computing believe biology may start to solve some of these problems. One of the most ambitious attempts to test that idea is SpiNNaker Spiking Neural Network Architecture https://www.scieng.manchester.ac.uk/tomorrowlabs/spinnaker/ . Developed over decades by researchers led by Professor Steve Furber, SpiNNaker is the world's largest neuromorphic computing platform, incorporating more than one million ARM processors and capable of modelling spiking neural networks at the scale of a mouse brain in biological real time. It is a platform that allows researchers to study everything from neuroscience to artificial intelligence, while also helping to investigate new approaches to energy-efficient computing and brain-inspired AI. A new thought for computing So, while SpiNNaker was designed to explore a deceptively simple question what happens when a computer is built to work more like a brain? , new projects emerging from Manchester’s International Centre for Neuromorphic Systems Manchester ICNS https://www.icns.manchester.ac.uk/ , are expanding that thought to explore how brain-inspired computing might be used in practical systems beyond the laboratory. One project uses biology-inspired computing https://aida4edge.elfak.rs/ to analyse information directly at the sensor, allowing devices to respond without sending vast quantities of data elsewhere for processing. Another explores wearable technologies https://www.tommys.org/research/research-topics/stillbirth-research/testing-fehemo-vest-monitor-babys-health that could help monitor foetal and maternal health. Other projects point to more tangible settings: vision sensors https://www.nimbleai.eu/ that process information as events unfold, monitoring systems for difficult environments https://www-users.york.ac.uk/~mt540/edgy-organism/index.html about . The applications of neuromorphic computing are varied, and they may seem far removed from the room-sized computers of the 1950s, but they return to a familiar Manchester habit: questioning accepted assumptions and asking whether we can do things differently. From Ferranti to the future of brain-inspired systems So, while neuromorphic computing may still be in its nascent stages, researchers believe it offers a promising way to rethink how information is sensed, processed and interpreted and allows us to rethink the relationship between machines, memory and intelligence. Seventy-five years ago, Alan Turing posed the question “can machines think?” and an industrial-academic collaboration saw the Ferranti Mark I transform the computer from an experimental machine into a practical technology. Today, through SpiNNaker and the International Centre for Neuromorphic Systems, Manchester's researchers are pursuing a familiar idea: that progress begins when accepted assumptions are challenged. Meet the researchers Professor André van Schaik https://research.manchester.ac.uk/en/persons/andr%C3%A9-van-schaik/ – linking neuromorphic engineering with intelligent sensors and computational neuroscience. Professor Piotr Dudek https://research.manchester.ac.uk/en/persons/p.dudek/ – vision chips, integrated circuits and brain-inspired sensing systems. Dr Davide Bertozzi https://research.manchester.ac.uk/en/persons/davide-bertozzi/ – exploring the processor and interconnection architectures needed to support efficient neuromorphic hardware. Dr Jayawan Wijekoon https://research.manchester.ac.uk/en/persons/jayawan.wijekoon/ – wearable and flexible electronics for health monitoring. Dr Oliver Rhodes https://research.manchester.ac.uk/en/persons/oliver.rhodes/ and Dr Luca Peres https://research.manchester.ac.uk/en/persons/luca.peres-2/ – low-energy computing, spiking neural networks and the architectures needed to run them.