Research from Imperial College London has shown that it’s possible to perform artificial intelligence (AI) using tiny nanomagnets that interact like neurons in the brain. The new method could slash the energy cost of AI.
The team has produced the first proof that networks of nanomagnets can be used to perform AI-esque processing. The researchers showed nanomagnets can be used for “time-series prediction” tasks (e.g., predicting and regulating insulin levels in diabetic patients).
AI 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 math used to power neural networks was originally invented by physicists to describe the way magnets interact, but back then it was too difficult to use magnets directly as researchers didn’t know how to input data in and retrieve information.
Software run on traditional silicon-based computers was used to simulate the magnet interactions, in turn simulating the brain. Now, the team has been able to use the magnets to process and store data — cutting out the software simulation and potentially offering large energy savings.
Nanomagnets can come in various “states,” depending on their direction. Applying a magnetic field to a network of nanomagnets changes the magnets’ state based on the properties of the input field but also on the states of surrounding magnets.
The team was then able to design a technique to count the number of magnets in each state once the field has passed through, giving the “answer.”
“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,” the study’s Co-First Author Dr. Jack Gartside said. “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.”
AI is now used in a range of contexts, but training AI to do even relatively simple tasks can take huge loads of energy. For instance, training AI to solve a Rubik’s cube took the energy equivalent of two nuclear power stations running for an hour. This innovation could make nanomagnetic computing up to 100,000 times more efficient than conventional computing.
The team will next teach the system using real-world data, such as ECG signals, and hopes to make it into a real computing device. Eventually, magnetic systems could be integrated into conventional computers to improve their energy efficiency.
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’s being collected, rather than sending it back to large data centers. Also, they could be used on wearable devices to process biometric data on the body (e.g., predicting and regulating insulin levels for diabetic people or detecting abnormal heartbeats).
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