Loughborough University physicists have developed a device that can process data that changes over time directly in hardware, rather than relying on software running on conventional computers. Their latest research, published in Advanced Intelligent Systems, suggests the approach could be up to around 2000 times more energy efficient than conventional software-based methods. “This is exciting because it shows we can rethink how AI systems are built,” said Senior Lecturer in Physics, Dr Pavel Borisov, who led the research team funded by the Engineering and Physical Sciences Research Council (EPSRC). “By using physical processes instead of relying entirely on software, we can dramatically reduce the energy needed for these kinds of tasks.” How it works Many real-world AI problems involve processing data that changes over time and identifying patterns to predict what happens next – for example in weather systems, biological processes or sensor data. A technique called reservoir computing is often used for this, where incoming data is transformed into a form that makes patterns easier to detect and predict, typically using software. The Loughborough device performs this type of computation directly in hardware. It is a type of memristor – an electronic component that can store information about past inputs – made of nanoporous oxide. It contains random nanopores that create multiple electrical pathways, and these pathways act like the hidden processing layer of a neural network, allowing the material itself to carry out part of the computation. Study findings In their study, the researchers showed that the device can process time-dependent data and, when its output is fed into a linear computer model, can be used to identify patterns and make short-term predictions. They tested the system using the Lorenz-63 system – a well-known mathematical model of chaos linked to the “butterfly effect”, where small changes can lead