Tag Archives: Daan M. Arroo

Tiny nanomagnets interact like neurons in the brain for low energy artificial intelligence (brainlike) computing

Saving energy is one of the main drivers for the current race to make neuromorphic (brainlike) computers as this May 5, 2022 news item on Nanowerk comments, Note: Links have been removed,

Researchers have shown it is possible to perform artificial intelligence using tiny nanomagnets that interact like neurons in the brain.

The new method, developed by a team led by Imperial College London researchers, could slash the energy cost of artificial intelligence (AI), which is currently doubling globally every 3.5 months. [emphasis mine]

In a paper published in Nature Nanotechnology (“Reconfigurable training and reservoir computing in an artificial spin-vortex ice via spin-wave fingerprinting”), the international team have produced the first proof that networks of nanomagnets can be used to perform AI-like processing. The researchers showed nanomagnets can be used for ‘time-series prediction’ tasks, such as predicting and regulating insulin levels in diabetic patients.

A May 5, 2022 Imperial College London (ICL) press release (also on EurekAlert) by Hayley Dunning, which originated the news item delves further into the research,

Artificial intelligence that uses ‘neural networks’ aims to replicate the way parts of the brain work, where neurons talk to each other to process and retain information. A lot of the maths used to power neural networks was originally invented by physicists to describe the way magnets interact, but at the time it was too difficult to use magnets directly as researchers didn’t know how to put data in and get information out.

Instead, software run on traditional silicon-based computers was used to simulate the magnet interactions, in turn simulating the brain. Now, the team have been able to use the magnets themselves to process and store data – cutting out the middleman of the software simulation and potentially offering enormous energy savings.

Nanomagnetic states

Nanomagnets can come in various ‘states’, depending on their direction. Applying a magnetic field to a network of nanomagnets changes the state of the magnets based on the properties of the input field, but also on the states of surrounding magnets.

The team, led by Imperial Department of Physics researchers, were then able to design a technique to count the number of magnets in each state once the field has passed through, giving the ‘answer’.

Co-first author of the study Dr Jack Gartside said: “We’ve been trying to crack the problem of how to input data, ask a question, and get an answer out of magnetic computing for a long time. Now we’ve proven it can be done, it paves the way for getting rid of the computer software that does the energy-intensive simulation.”

Co-first author Kilian Stenning added: “How the magnets interact gives us all the information we need; the laws of physics themselves become the computer.”

Team leader Dr Will Branford said: “It has been a long-term goal to realise computer hardware inspired by the software algorithms of Sherrington and Kirkpatrick. It was not possible using the spins on atoms in conventional magnets, but by scaling up the spins into nanopatterned arrays we have been able to achieve the necessary control and readout.”

Slashing energy cost

AI is now used in a range of contexts, from voice recognition to self-driving cars. But training AI to do even relatively simple tasks can take huge amounts of energy. For example, training AI to solve a Rubik’s cube took the energy equivalent of two nuclear power stations running for an hour.

Much of the energy used to achieve this in conventional, silicon-chip computers is wasted in inefficient transport of electrons during processing and memory storage. Nanomagnets however don’t rely on the physical transport of particles like electrons, but instead process and transfer information in the form of a ‘magnon’ wave, where each magnet affects the state of neighbouring magnets.

This means much less energy is lost, and that the processing and storage of information can be done together, rather than being separate processes as in conventional computers. This innovation could make nanomagnetic computing up to 100,000 times more efficient than conventional computing.

AI at the edge

The team will next teach the system using real-world data, such as ECG signals, and hope to make it into a real computing device. Eventually, magnetic systems could be integrated into conventional computers to improve energy efficiency for intense processing tasks.

Their energy efficiency also means they could feasibly be powered by renewable energy, and used to do ‘AI at the edge’ – processing the data where it is being collected, such as weather stations in Antarctica, rather than sending it back to large data centres.

It also means they could be used on wearable devices to process biometric data on the body, such as predicting and regulating insulin levels for diabetic people or detecting abnormal heartbeats.

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

Reconfigurable training and reservoir computing in an artificial spin-vortex ice via spin-wave fingerprinting by Jack C. Gartside, Kilian D. Stenning, Alex Vanstone, Holly H. Holder, Daan M. Arroo, Troy Dion, Francesco Caravelli, Hidekazu Kurebayashi & Will R. Branford. Nature Nanotechnology (2022) DOI: https://doi.org/10.1038/s41565-022-01091-7 Published 05 May 2022

This paper is behind a paywall.

Paving the way for hardware neural networks?

I’m glad the Imperial College of London (ICL; UK) translated this research into something I can, more or less, understand because the research team’s title for their paper would have left me ‘confuzzled’ .Thank you for this November 20, 2017 ICL press release (also on EurekAlert) by Hayley Dunning,

Researchers have shown how to write any magnetic pattern desired onto nanowires, which could help computers mimic how the brain processes information.

Much current computer hardware, such as hard drives, use magnetic memory devices. These rely on magnetic states – the direction microscopic magnets are pointing – to encode and read information.

Exotic magnetic states – such as a point where three south poles meet – represent complex systems. These may act in a similar way to many complex systems found in nature, such as the way our brains process information.

Computing systems that are designed to process information in similar ways to our brains are known as ‘neural networks’. There are already powerful software-based neural networks – for example one recently beat the human champion at the game ‘Go’ – but their efficiency is limited as they run on conventional computer hardware.

Now, researchers from Imperial College London have devised a method for writing magnetic information in any pattern desired, using a very small magnetic probe called a magnetic force microscope.

With this new writing method, arrays of magnetic nanowires may be able to function as hardware neural networks – potentially more powerful and efficient than software-based approaches.

The team, from the Departments of Physics and Materials at Imperial, demonstrated their system by writing patterns that have never been seen before. They published their results today [November 20, 2017] in Nature Nanotechnology.

Interlocking hexagon patterns with complex magnetisation

‘Hexagonal artificial spin ice ground state’ – a pattern never demonstrated before. Coloured arrows show north or south polarisation

Dr Jack Gartside, first author from the Department of Physics, said: “With this new writing method, we open up research into ‘training’ these magnetic nanowires to solve useful problems. If successful, this will bring hardware neural networks a step closer to reality.”

As well as applications in computing, the method could be used to study fundamental aspects of complex systems, by creating magnetic states that are far from optimal (such as three south poles together) and seeing how the system responds.

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

Realization of ground state in artificial kagome spin ice via topological defect-driven magnetic writing by Jack C. Gartside, Daan M. Arroo, David M. Burn, Victoria L. Bemmer, Andy Moskalenko, Lesley F. Cohen & Will R. Branford. Nature Nanotechnology (2017) doi:10.1038/s41565-017-0002-1 Published online: 20 November 2017

This paper is behind a paywall.

*Odd spacing eliminated and a properly embedded video added on February 6, 2018 at 18:16 hours PT.