Information and communication technologies (ICTs) driven by artificial intelligence (AI) is generating data at an unprecedented rate
Information and communication technologies (ICTs) driven by artificial intelligence (AI) is generating data at an unprecedented rate.
Every internet search, AI-generated image, recommendation, scientific simulation and large language model creates and processes enormous amounts of information that must be stored, transferred and analysed. As AI continues to expand across every sector of society, global demand for data storage and computing is rising dramatically.
This rapid growth comes at a significant cost: energy consumption. Data centres already consume vast amounts of electricity, and demand is expected to increase sharply over the coming decades. Without major technological advances, ICTs could account for a substantial fraction of global electricity use and carbon emissions, making energy-efficient computing one of the defining scientific challenges of our time.
Researchers at the University of Edinburgh have now developed a new theoretical framework that could help address this challenge by dramatically reducing the energy required to store and manipulate digital information (bits, represented as “0”s and “1”s) in future magnetic memory technologies.
Rather than relying on conventional approaches to designing magnetic switching processes (which is the basic mechanism behind data manipulation), the team employed Optimal Control Theory, a branch of mathematics that identifies the most efficient way to achieve a desired outcome. Their framework designs ultrafast magnetic-field pulses that switch magnetic states using the minimum possible energy while accounting for realistic experimental constraints.
Computer simulations indicate that this approach could reduce switching energies by several orders of magnitude compared with today’s leading memory technologies, including DRAM, STT-MRAM and emerging SOT-MRAM devices. Remarkably, the predicted energy consumption brings future magnetic memories much closer to the Landauer limit (the fundamental thermodynamic limit) defining the minimum amount of energy required to process a single bit of information.
The framework, published in Advanced Materials, also provides practical guidelines for its future implementation through optimized device architectures and magnetic-field delivery schemes, offering a realistic pathway towards experimental validation.
Dr Elton Santos from the Institute for Condensed Matter Physics and Complex Systems, University of Edinburgh, and who led the research, said:
“Every digital operation has an energy cost, and that cost becomes increasingly important as AI and data-intensive technologies continue to expand. Our work shows that, by carefully designing how a magnetic field changes in time, magnetisation can be switched far more efficiently than with conventional approaches.”
He continued:
“Although we first developed the theory using magnetic field pulses, the mathematics is far more versatile than that. The same framework can be adapted to electrical currents and even ultrafast laser pulses, which are among the most cutting-edge technologies for future data storage. That means the ideas developed here could have applications far beyond the systems we studied. It seems that we may have just found the next best thing.”