‘Nanomagnetic’ computing can provide low-energy AI, researchers show

magnetism

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

The new method, developed by a team led by researchers at Imperial College London, could dramatically reduce the energy cost from artificial intelligence (AI), which is currently doubling globally every 3.5 months.

In an article published today in Nature Nanotechnology, the international team produced the first proof that networks of nanomagnets can be used to perform AI-like processing. The researchers showed that 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 communicate with each other to process and retain information. Much of the math 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 input data and get information.

Instead, software running on traditional silicon-based computers was used to simulate magnetic interactions, in turn simulating the brain. Now the team has been able to use the magnets themselves to process and store data, cutting out the software simulation middleman and potentially offering huge energy savings.

nanomagnetic states

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

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

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

Co-author Kilian Stenning added: “The way 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 create computer hardware inspired by Sherrington and Kirkpatrick’s software algorithms. It was not possible to use the spins of atoms in conventional magnets, but by upscaling the spins in nanometer-patterned arrays, we’ve been able to achieve the necessary control and readout.”

Cut the cost of energy

AI is now used in a variety of contexts, from voice recognition to self-driving cars. But training AI to perform even relatively simple tasks can take enormous amounts of energy. For example, training the 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 accomplish this in conventional silicon-chip computers is wasted in inefficient electron transport during processing and storage in memory. However, nanomagnets are not based on the physical transport of particles such as electrons, but instead process and transfer information in the form of a ‘magnonic’ wave, where each magnet affects the state of neighboring magnets.

This means that much less energy is wasted and that 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 on the edge

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

Their energy efficiency it also means they could run on renewable energy Energyand used to do ‘AI at the edge’: process the data where it is collected, like weather stations in Antarctica, instead of sending it back to large data centers.

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


Learning magnets could lead to energy efficient data processing


More information:
Jack Gartside, Reconfigurable Training and Reservoir Computation in an Artificial Spin Vortex Ice via Spin Wave Fingerprinting, Nature Nanotechnology (2022). DOI: 10.1038/s41565-022-01091-7. www.nature.com/articles/s41565-022-01091-7

Citation: ‘Nanomagnetic’ computing can provide low-energy AI, researchers show (May 5, 2022) Accessed May 6, 2022 at https://phys.org/news/2022-05-nanomagnetic-low-energy -ai.html

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