Energy efficient computing with tiny magnetic swirls

Much of the energy used today is consumed in the form of electrical energy for processing and storing data and for operating the relevant equipment and terminal devices. According to forecasts, the level of energy used for these purposes will increase further in the future. Innovative concepts, such as neuromorphic computing, use power-saving approaches to address this problem. In a joint project undertaken by experimental and theoretical physicists from Johannes Gutenberg University Mainz (JGU) with funding from an ERC Synergy Grant, such an approach, known as Brownian reservoir calculation, has now been carried out. The results were also recently featured as a highlight of editors in the Devices scientific journal section Nature Communication.

Brownian computing uses ambient thermal energy

The Brownian reservoir calculation is a combination of two unconventional calculation methods. Brownian computing exploits the fact that computing processes generally operate at room temperature, so that it is possible to use the surrounding thermal energy and thus reduce electricity consumption. The thermal energy used in the computer system is essentially the random motion of particles, known as Brownian motion; which explains the name of this method of calculation.

Reservoir computing is ideal for exceptionally efficient data processing

Reservoir computing uses the complex response of a physical system to external stimuli, resulting in an extremely efficient way of processing data. Most of the calculations are done by the system itself, which does not require any additional power. Moreover, this type of tank computer can easily be customized to perform various tasks as there is no need to adjust the solid state system to meet specific requirements.

A team led by Professor Mathias Kläui from the Institute of Physics at the University of Mainz, supported by Professor Johan Mentink from Radboud University Nijmegen in the Netherlands, has now succeeded in developing a prototype combining these two methods. Calculation. This prototype is able to perform Boolean logic operations, which can be used as standard tests for reservoir calculation validation.

The solid-state system selected in this case consists of metallic thin layers with magnetic skyrmions. These magnetic vortices behave like particles and can be driven by electric currents. The behavior of skyrmions is influenced not only by the applied current but also by their own Brownian motion. This Brownian motion of skyrmions can result in greatly increased energy savings because the system is automatically reset after each operation and prepared for the next calculation.

First prototype developed in Mainz

Although there have been many theoretical concepts for skyrmion-based reservoir computing in recent years, the Mainz researchers only managed to develop the first working prototype by combining these concepts with the principle of l Brownian computing. “The prototype is lithographically easy to produce and can theoretically be reduced to a size of a few nanometers,” said experimental physicist Klaus Raab. “We owe our success to the excellent collaboration between experimental and theoretical physicists here at the University of Mainz,” stressed theoretical physicist Maarten Brems. Project coordinator Professor Mathias Kläui added: “I am delighted that the funding provided through a synergy grant from the European Research Council has enabled us to collaborate with outstanding colleagues from the Department of Theoretical Physics from Nijmegen, and it was this collaboration that resulted in our I see great potential in unconventional computing, an area that is also receiving significant support here in Mainz through funding from the Carl Zeiss Foundation for Emerging Algorithmic Intelligence Center.”

Related links:
https://www.klaeui-lab.physik.uni-mainz.de – Kläui Laboratory at JGU Institute of Physics;
https://www.komet1.physik.uni-mainz.de – Statistical Physics and Soft Matter Theory Group at the JGU Institute of Physics;
https://topdyn.uni-mainz.de/ – Top level research area “TopDyn – Dynamics and Topology” at JGU;
https://3d-magic-project.eu/ – ERC Synergy Grant 3D MAGiC;
https://emergent-ai.uni-mainz.de/ – Center for Emergent Algorithmic Intelligence at JGU

Read more:
https://www.uni-mainz.de/presse/aktuell/15662_ENG_HTML.php – press release “Obstacle course for microscopic eddies” (July 4, 2022);
https://www.uni-mainz.de/presse/aktuell/14647_ENG_HTML.php – press release “Efficient read-out in antiferromagnetic spintronics” (25 Nov. 2021);
https://www.uni-mainz.de/presse/aktuell/13181_ENG_HTML.php – press release “Magnetic eddies in confined spaces” (March 4, 2021);
https://www.uni-mainz.de/presse/aktuell/12071_ENG_HTML.php – press release “Magnetic vortices crystallize in two dimensions” (9 September 2020)