Memristive control of mutual spin

It may be my imagination but it seems I’m stumbling across more research on neuromorphic (brainlike) computing than usual this year. In May 2022 alone I stumbled across three items. Today (August 24, 2022), here’s a May 14, 2022 news item on Nanowerk describes some work from the University of Gothenburg (Sweden),

Artificial Intelligence (AI) is making it possible for machines to do things that were once considered uniquely human. With AI, computers can use logic to solve problems, make decisions, learn from experience and perform human-like tasks. However, they still cannot do this as effectively and energy efficiently as the human brain.

Research conducted with support from the EU-funded TOPSPIN and SpinAge projects has brought scientists a step closer to achieving this goal.

“Finding new ways of performing calculations that resemble the brain’s energy-efficient processes has been a major goal of research for decades,” observes Prof. Johan Åkerman of TOPSIN project host University of Gothenburg, Sweden. “Cognitive tasks, like image and voice recognition, require significant computer power, and mobile applications, in particular, like mobile phones, drones and satellites, require energy efficient solutions,” continues Prof. Åkerman, who is also the founder and CEO of SpinAge project partner NanOsc, also in Sweden.

A May 13, 2022 CORDIS press release, which originated the news item, provides more detail,

The research team succeeded in combining a memory function and a calculation function in one component for the very first time. The achievement is described in their study published in the journal ‘Nature Materials’. The memory and calculation functions were combined by linking oscillator networks and memristors – the two main tools needed to carry out advanced calculations. Oscillators are described as oscillating circuits capable of performing calculations. Memristors, short for memory resistors, are electronic devices whose resistance can be programmed and remains stored. In other words, the memristor’s resistance performs a memory function by remembering what value it had when the device was powered on.

A major development

Prof. Åkerman comments on the discovery: “This is an important breakthrough because we show that it is possible to combine a memory function with a calculating function in the same component. These components work more like the brain’s energy-efficient neural networks, allowing them to become important building blocks in future, more brain-like computers.”

As reported in the news item, Prof. Åkerman believes this achievement will lead to the development of technologies that are faster, easier to use and less energy-consuming. Also, the fact that hundreds of components can fit into an area the size of a single bacterium could have a significant impact on smaller applications. “More energy-efficient calculations could lead to new functionality in mobile phones. An example is digital assistants like Siri or Google. Today, all processing is done by servers since the calculations require too much energy for the small size of a phone. If the calculations could instead be performed locally, on the actual phone, they could be done faster and easier without a need to connect to servers.”

Prof. Åkerman concludes: “The more energy-efficiently that cognitive calculations can be performed, the more applications become possible. That’s why our study really has the potential to advance the field.” The TOPSPIN (Topotronic multi-dimensional spin Hall nano-oscillator networks) and SpinAge (Weighted Spintronic-Nano-Oscillator-based Neuromorphic Computing System Assisted by laser for Cognitive Computing) projects end in 2024.

For more information, please see:
TOPSPIN project
SpinAge project

The University of Gothenburg first announced the research in a November 29, 2021 press release on EurekAlert,

Research has long strived to develop computers to work as energy efficiently as our brains. A study, led by researchers at the University of Gothenburg, has succeeded for the first time in combining a memory function with a calculation function in the same component. The discovery opens the way for more efficient technologies, everything from mobile phones to self-driving cars.

In recent years, computers have been able to tackle advanced cognitive tasks, like language and image recognition or displaying superhuman chess skills, thanks in large part to artificial intelligence (AI). At the same time, the human brain is still unmatched in its ability to perform tasks effectively and energy efficiently.

“Finding new ways of performing calculations that resemble the brain’s energy-efficient processes has been a major goal of research for decades. Cognitive tasks, like image and voice recognition, require significant computer power, and mobile applications, in particular, like mobile phones, drones and satellites, require energy efficient solutions,” says Johan Åkerman, professor of applied spintronics at the University of Gothenburg.

Important breakthrough
Working with a research team at Tohoko University, Åkerman led a study that has now taken an important step forward in achieving this goal. In the study, now published in the highly ranked journal Nature Materials, the researchers succeeded for the first time in linking the two main tools for advanced calculations: oscillator networks and memristors.

Åkerman describes oscillators as oscillating circuits that can perform calculations and that are comparable to human nerve cells. Memristors are programable resistors that can also perform calculations and that have integrated memory. This makes them comparable to memory cells. Integrating the two is a major advancement by the researchers.

“This is an important breakthrough because we show that it is possible to combine a memory function with a calculating function in the same component. These components work more like the brain’s energy-efficient neural networks, allowing them to become important building blocks in future, more brain-like computers.”

Enables energy-efficient technologies
According to Johan Åkerman, the discovery will enable faster, easier to use and less energy consuming technologies in many areas. He feels that it is a huge advantage that the research team has successfully produced the components in an extremely small footprint: hundreds of components fit into an area equivalent to a single bacterium. This can be of particular importance in smaller applications like mobile phones.

“More energy-efficient calculations could lead to new functionality in mobile phones. An example is digital assistants like Siri or Google. Today, all processing is done by servers since the calculations require too much energy for the small size of a phone. If the calculations could instead be performed locally, on the actual phone, they could be done faster and easier without a need to connect to servers.”

He notes self-driving cars and drones as other examples of where more energy-efficient calculations could drive developments.

“The more energy-efficiently that cognitive calculations can be performed, the more applications become possible. That’s why our study really has the potential to advance the field.”

Here’s a link to and a citation for the paper,

Memristive control of mutual spin Hall nano-oscillator synchronization for neuromorphic computing by Mohammad Zahedinejad, Himanshu Fulara, Roman Khymyn, Afshin Houshang, Mykola Dvornik, Shunsuke Fukami, Shun Kanai, Hideo Ohno & Johan Åkerman. Nature Materials volume 21, pages 81–87 (2022) DOI: https://doi.org/10.1038/s41563-021-01153-6 First Published: 29 November 2021 Issue Date: January 2022

This paper is behind a paywall.

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