'Nanomagnetic' computing can provide low-energy AI

Date:
May 5, 2022
Source:
Imperial College London
Summary:
Researchers have shown it is possible to perform artificial
intelligence using tiny nanomagnets that interact like neurons in
the brain.



FULL STORY ========================================================================== 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.

In a paper published today in Nature Nanotechnology, 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.

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.


========================================================================== Story Source: Materials provided by Imperial_College_London. Original
written by Hayley Dunning. Note: Content may be edited for style and
length.


========================================================================== Journal Reference:
1. Jack C. Gartside, Kilian D. Stenning, Alex Vanstone, Holly
H. Holder,
Daan M. Arroo, Troy Dion, Francesco Caravelli, Hidekazu Kurebayashi,
Will R. Branford. Reconfigurable training and reservoir computing in
an artificial spin-vortex ice via spin-wave fingerprinting. Nature
Nanotechnology, 2022; DOI: 10.1038/s41565-022-01091-7 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/05/220505114646.htm

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