Revolutionizing AI: Chiral Magnets Pave the Way for Energy-Efficient Brain-Like Computing

A groundbreaking study from researchers at UCL and Imperial College London is bringing us closer to a new kind of brain-inspired computing—one that could slash energy use by harnessing the natural physics of exotic materials.

In the study, published in Nature Materials, scientists explored how chiral magnets, materials with a natural twist in their structure, can be used to perform machine learning tasks. By tweaking the temperature and applying magnetic fields, the researchers were able to fine-tune the material’s behavior to suit different types of computation.

This innovative method, called physical reservoir computing, mimics how the brain processes information—but with a major twist. Until now, this type of computing has been held back by a major limitation: most materials can only handle a narrow set of tasks. But by making chiral magnets adaptable, the research team opened the door to far more flexible and efficient computing.

“This work brings us a step closer to realizing the full potential of physical reservoirs to create computers that not only require significantly less energy,” said lead author Dr. Oscar Lee from UCL, “but also adapt their computational properties to perform optimally across various tasks, just like our brains.”

Why It Matters

Traditional computers burn through electricity, especially when powering AI. That’s because data is constantly shuffled between separate units for storage and processing—a system that wastes energy and generates heat. In fact, training just one large AI model can emit hundreds of tons of carbon dioxide.

But with adaptable, energy-efficient materials like chiral magnets, researchers hope to build smarter, greener computers that can handle complex tasks without the environmental cost.

The next step? Finding the right materials and designs to scale this futuristic tech into everyday use.

Neuromorphic Computing: A Sustainable Approach

Physical reservoir computing is one of several neuromorphic (or brain-inspired) approaches that aim to remove the need for distinct memory and processing units, facilitating more efficient ways to process data. In addition to being a more sustainable alternative to conventional computing, physical reservoir computing could be integrated into existing circuitry to provide additional capabilities that are also energy efficient.

In the study, involving researchers in Japan and Germany, the team used a vector network analyzer to determine the energy absorption of chiral magnets at different magnetic field strengths and temperatures ranging from -269 °C to room temperature.

They found that different magnetic phases of chiral magnets excelled at different types of computing tasks. The skyrmion phase, where magnetized particles are swirling in a vortex-like pattern, had a potent memory capacity apt for forecasting tasks. The conical phase, meanwhile, had little memory, but its non-linearity was ideal for transformation tasks and classification – for instance, identifying if an animal is a cat or dog.

Co-author Dr. Jack Gartside, of Imperial College London, said: “Our collaborators at UCL in the group of Professor Hidekazu Kurebayashi recently identified a promising set of materials for powering unconventional computing. These materials are special as they can support an especially rich and varied range of magnetic textures. Working with the lead author, Dr. Oscar Lee, the Imperial College London group [led by Dr Gartside, Kilian Stenning, and Professor Will Branford] designed a neuromorphic computing architecture to leverage the complex material properties to match the demands of a diverse set of challenging tasks. This gave great results, and showed how reconfiguring physical phases can directly tailor neuromorphic computing performance.”

Reference: “Task-adaptive physical reservoir computing” by Oscar Lee, Tianyi Wei, Kilian D. Stenning, Jack C. Gartside, Dan Prestwood, Shinichiro Seki, Aisha Aqeel, Kosuke Karube, Naoya Kanazawa, Yasujiro Taguchi, Christian Back, Yoshinori Tokura, Will R. Branford and Hidekazu Kurebayashi, 13 November 2023, Nature Materials.
DOI: 10.1038/s41563-023-01698-8

The work also involved researchers at the University of Tokyo and Technische Universität München and was supported by the Leverhulme Trust, Engineering and Physical Sciences Research Council (EPSRC), Imperial College London President’s Excellence Fund for Frontier Research, Royal Academy of Engineering, the Japan Science and Technology Agency, Katsu Research Encouragement Award, Asahi Glass Foundation, and the DFG (German Research Foundation).

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