Artificial intelligence is transforming how information is generated, processed and stored, but its rapid expansion is also driving an unprecedented demand for computing power and electricity. Developing hardware that can process information more efficiently is therefore becoming one of the major technological challenges of the AI era.
Researchers from an international collaboration involving the University of Edinburgh have demonstrated a new approach to brain-inspired computing based on tiny magnetic structures, known as skyrmions . The research, published in Advanced Materials shows how collective transformations of these magnetic structures can be used to create reliable artificial synapses that operate at room temperature.
The human brain is remarkably energy efficient because memory and information processing occur together through networks of neurons and synapses. Conventional computers, by contrast, continuously transfer information between physically separated processing and memory units. Neuromorphic computing seeks to overcome this limitation by developing electronic devices whose behaviour more closely resembles biological neural networks.
Magnetic skyrmions are particularly attractive for this purpose. They are nanoscale, vortex-like arrangements of magnetic moments that behave as stable information carriers. Their small dimensions, non-volatile nature and ability to respond to external stimuli have made them promising building blocks for future memory and computing technologies. However, previous approaches to skyrmion-based artificial synapses have often relied on creating or destroying individual skyrmions. Because these processes can be inherently probabilistic, obtaining a predictable and reproducible response remains challenging.
The new study takes a fundamentally different approach. Using the two-dimensional van der Waals ferromagnet Fe ₃ GaTe ₂ , the researchers exploit a collective transformation of the magnetic state from a skyrmion lattice into stripe-like magnetic domains . Rather than depending on the stochastic behavior of individual skyrmions, large populations of magnetic textures evolve collectively and deterministically.
This transformation produces a linear and highly reproducible change in the material's anomalous Hall resistance, an electrical signal that can be used to represent the strength, or “weight," of an artificial synapse . By changing the duration of the applied electrical pulses, the researchers can tune this synaptic weight, creating multiple information states and enabling the multiply-accumulate operations that underpin modern neural networks.
Importantly, the effect occurs at room temperature , overcoming one of the major obstacles to translating many emerging quantum and magnetic phenomena into practical technologies. This means that this new synapse can be implemented in real world applications promptly.
Towards low-energy artificial intelligence
The potential energy savings are significant. When scaled towards future device dimensions, the researchers estimate an energy consumption of approximately 0.66 picojoules per operation , comparable with leading emerging memristive technologies such as resistive random-access memory and phase-change memory.
To test whether the device behavior could be useful for real computing tasks, the researchers incorporated its measured characteristics into a hardware-informed quantized neural network. When used to recognize handwritten digits, the simulated network achieved an accuracy of approximately 96.1% .
"The remarkable efficiency of the human brain continues to inspire us to rethink how computers store and process information ," said Dr Elton Santos from the University of Edinburgh's School of Physics and Astronomy, one of the lead authors of this research. "Here, rather than manipulating magnetic skyrmions individually, we exploit their collective behaviour. This gives us a much more deterministic and reproducible way of controlling information while retaining the advantages of these remarkably small topological magnetic structures."
The study brings together expertise in materials synthesis, magnetic characterisation, electrical measurements, theoretical modelling and neuromorphic computing. The researchers believe the principle of exploiting collective magnetic transformations , rather than controlling individual magnetic objects one at a time, could provide a broader strategy for developing robust and scalable spin-based computing technologies.
Ultimately, the findings point towards a future in which the unusual physics of quantum materials could be harnessed not simply to improve existing computer components, but to create fundamentally different forms of hardware—devices that store and process information collectively and could help make future AI systems more energy efficient.
For further information, please contact: Rhona Crawford, Press and PR Office, mb: 07876 391498, email: rhona.crawford@ed.ac.uk
Advanced Materials
Room-Temperature Skyrmionic Synapse in 2D Ferromagnet Fe3GaTe2 Operating via Collective Spin Texture Transformation