Nanotechnology Now

Our NanoNews Digest Sponsors
Heifer International



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

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

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

London, UK | Posted on May 6th, 2022

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.

####

For more information, please click here

Contacts:
Hayley Dunning
Imperial College London

Office: 020-759-42412

Copyright © Imperial College London

If you have a comment, please Contact us.

Issuers of news releases, not 7th Wave, Inc. or Nanotechnology Now, are solely responsible for the accuracy of the content.

Bookmark:
Delicious Digg Newsvine Google Yahoo Reddit Magnoliacom Furl Facebook

Related Links

ARTICLE TITLE

Related News Press

News and information

Solvent study solves solar cell durability puzzle: Rice-led project could make perovskite cells ready for prime time September 23rd, 2022

Heat-resistant nanophotonic material could help turn heat into electricity: The key to beating the heat is degrading the materials in advance September 23rd, 2022

Chicago Quantum Exchange welcomes six new partners highlighting quantum technology solutions, from Chicago and beyond September 23rd, 2022

“Twisty” photons could turbocharge next-gen quantum communication: Team’s on-chip technology uses orbital angular momentum to encode more information into a single photon September 23rd, 2022

Magnetism/Magnons

Scientists take control of magnetism at the microscopic level: Neutrons reveal remarkable atomic behavior in thermoelectric materials for more efficient conversion of heat into electricity August 26th, 2022

'Frustrated' nanomagnets order themselves through disorder: Interactions between alternating layers of exotic, 2D material create 'entropy-driven order' in a structured system of magnets at equilibrium April 8th, 2022

Tuning the bonds of paired quantum particles to create dissipationless flow: A tunable platform made from atomically thin materials may help researchers figure out how to create a robust quantum condensate that can flow without losing energy January 14th, 2022

Terahertz light-driven spin-lattice control: A new potential path to faster and more efficient data storage January 7th, 2022

Possible Futures

Chicago Quantum Exchange welcomes six new partners highlighting quantum technology solutions, from Chicago and beyond September 23rd, 2022

“Twisty” photons could turbocharge next-gen quantum communication: Team’s on-chip technology uses orbital angular momentum to encode more information into a single photon September 23rd, 2022

Wrapping of nanosize copper cubes can help convert carbon dioxide into other chemicals September 23rd, 2022

Upgrading your computer to quantum September 23rd, 2022

Discoveries

New technique allows researchers to scrape beyond the surface of nanomaterials: Using a new secondary-ion mass spectrometry technique, research are getting a fresh look at MXenes and MAX phases September 23rd, 2022

“Twisty” photons could turbocharge next-gen quantum communication: Team’s on-chip technology uses orbital angular momentum to encode more information into a single photon September 23rd, 2022

Upgrading your computer to quantum September 23rd, 2022

Key element for a scalable quantum computer: Physicists from Forschungszentrum Jülich and RWTH Aachen University demonstrate electron transport on a quantum chip September 23rd, 2022

Announcements

Heat-resistant nanophotonic material could help turn heat into electricity: The key to beating the heat is degrading the materials in advance September 23rd, 2022

Chicago Quantum Exchange welcomes six new partners highlighting quantum technology solutions, from Chicago and beyond September 23rd, 2022

“Twisty” photons could turbocharge next-gen quantum communication: Team’s on-chip technology uses orbital angular momentum to encode more information into a single photon September 23rd, 2022

Wrapping of nanosize copper cubes can help convert carbon dioxide into other chemicals September 23rd, 2022

Interviews/Book Reviews/Essays/Reports/Podcasts/Journals/White papers/Posters

New technique allows researchers to scrape beyond the surface of nanomaterials: Using a new secondary-ion mass spectrometry technique, research are getting a fresh look at MXenes and MAX phases September 23rd, 2022

Modulating MoSe2 functional plane via doping-defect engineering strategy to develop conductive and electrocatalytic mediators in Li-S batteries September 23rd, 2022

Solvent study solves solar cell durability puzzle: Rice-led project could make perovskite cells ready for prime time September 23rd, 2022

Key element for a scalable quantum computer: Physicists from Forschungszentrum Jülich and RWTH Aachen University demonstrate electron transport on a quantum chip September 23rd, 2022

Artificial Intelligence

New chip ramps up AI computing efficiency August 19th, 2022

Artificial Intelligence Centered Cancer Nanomedicine: Diagnostics, Therapeutics and Bioethics June 3rd, 2022

Development of low-power and high-efficiency artificial sensory neurons: 3T-OTS device to simulate the efficient information processing method of the human brain. A green light for the development of sensor-AI combined next-generation artificial intelligence “to be used in life a April 8th, 2022

Artificial neurons go quantum with photonic circuits: Quantum memristor as missing link between artificial intelligence and quantum computing March 25th, 2022

NanoNews-Digest
The latest news from around the world, FREE




  Premium Products
NanoNews-Custom
Only the news you want to read!
 Learn More
NanoStrategies
Full-service, expert consulting
 Learn More











ASP
Nanotechnology Now Featured Books




NNN

The Hunger Project