Scientists Develop Ultrafast Magnetic-Field Pulses to Slash Computer Memory Energy Use by 100x

Scientists find a way to slash computer memory energy use by orders of magnitude

Artificial intelligence (AI) and other information and communication technologies (ICTs) are producing and processing data at a scale never seen before. Internet searches, AI-generated images, recommendation systems, scientific simulations, and large language models all depend on enormous amounts of information being created, moved, stored, and analyzed. As AI becomes more deeply integrated into everyday life, industry, and science, the global need for computing power and data storage continues to climb. AI's Growing Energy Demand That expansion also brings a major challenge: electricity use. Data centers already require huge amounts of power, and their energy demands are expected to rise substantially in the coming decades. Without significant improvements in efficiency, ICTs could eventually represent a sizable share of worldwide electricity consumption and carbon emissions. Finding ways to make computing more energy efficient is therefore becoming increasingly important as demand for digital services accelerates. Researchers at the University of Edinburgh have developed a new theoretical framework that could help reduce the amount of energy needed to store and manipulate digital information (bits, represented as "0"s and "1"s) in future magnetic memory technologies. A More Efficient Way to Switch Magnetic Memory At the heart of magnetic memory is the ability to switch magnetic states, which allows digital information to be changed and controlled. Instead of using conventional methods for designing magnetic switching processes (which is the basic mechanism behind data manipulation), the researchers turned to Optimal Control Theory, a mathematical approach used to determine the most efficient way to reach a specific goal. Using this method, the team created a framework for designing ultrafast magnetic-field pulses that can switch magnetic states while consuming as little energy as possible. The calculations also take realistic experimental limitations into account, making the approach more relevant to potential future devices. Moving Closer to a Fundamental Energy Limit Computer simulations suggest that the method could lower switching energy by several orders of magnitude compared with leading memory technologies used or being developed today, including DRAM, STT-MRAM and emerging SOT-MRAM devices. Even more strikingly, the predicted energy requirements move future magnetic memory much closer to the Landauer limit (the fundamental thermodynamic limit) defining the minimum amount of energy required to process a single bit of information. That limit represents a fundamental boundary imposed by physics, so approaching it would mark a major advance in the effort to make computing as energy efficient as possible. The framework, described in Advanced Materials, also goes beyond theoretical calculations. It includes practical guidance for possible implementation, including optimized device designs and methods for delivering magnetic fields. These recommendations could help researchers eventually test the concept experimentally. Potential Beyond Magnetic Fields Dr. Elton Santos from the Institute for Condensed Matter Physics and Complex Systems, University of Edinburgh, 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, magnetization 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."

Scientists develop 'ultrafast magnetic-field pulses' memory system that could cut AI data center energy use by 100x -- and even get close to hitting thermodynamic limits

* AI data centers currently use a huge amount of energy * Scientists have found a way to cut that energy bill by "orders of magnitude" * It involves using "ultrafast magnetic-field pulses" in RAM and storage It's no secret that the current artificial intelligence (AI) boom is leading to record levels of energy consumption -- all that computing power needs to be fueled somehow -- and it's causing much controversy among the communities that are impacted whenever a new data center lays down its roots in their neighborhoods. Now, though, scientists think they've come across a way that could radically slash the energy requirements of memory and storage, with potentially massive consequences for the future of the computing and AI industries. In a paper published in the Advanced Materials journal (via a press release in Science Daily), researchers at the University of Edinburgh in Scotland wrote that the breakthrough would fundamentally affect the way magnetic memory operates. Right now, switching its state allows magnetic memory to control digital information, but the current methods of doing so can be costly in terms of energy. Instead of using standard energy switching techniques, the researchers discovered an alternative that harnessed ultrafast magnetic field pulses that consumed far less energy. In fact, the paper claimed that the authors' approach could cut energy usage by "up to two orders of magnitude" -- in other words, a cut of around 100x, which is a significant reduction. The data centers of the future The AI revolution is only a few years old, but data center energy consumption has already become a significant issue. As the authors of the research paper put it, "Without significant improvements in efficiency, [information and communication technologies] could eventually represent a sizable share of worldwide electricity consumption and carbon emissions." AI is playing a large role in that trend thanks to both its voracious appetite for component production and the sizable emissions it produces. While the paper focused on magnetic memory, its theories could be applied to other fields, the authors believe. As study writer Dr. Elton J.G. Santos put it, "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." Although the research is still in the realm of theory, the paper's authors proposed a number of practical steps that could help others build prototypes and conduct experiments. That said, don't expect monumental changes any time soon. There is still a long road ahead before these ideas get put into practice, if they ever do. But given the promising results of the scientists' work, there is hope that the data centers of the future could be far less energy-intensive than those of today. Follow TechRadar on Google News and add us as a preferred source to get our expert news, reviews, and opinion in your feeds.