Brain-inspired electronic system could vastly reduce AI’s carbon footprint

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A wafer filled with memristors.

Extremely energy-efficient artificial intelligence is now closer to reality after a study by UCL researchers found a way to improve the accuracy of a brain-inspired computing system.

The system, which uses memristors to create artificial neural networks, is at least 1,000 times more energy efficient than conventional transistor-based AI hardware, but has until now been more prone to error.

Existing AI is extremely energy-intensive—training one AI model can generate 284 tons of carbon dioxide, equivalent to the lifetime emissions of five cars. Replacing the transistors that make up all digital devices with memristors, a novel electronic device first built in 2008, could reduce this to a fraction of a ton of carbon dioxide—equivalent to emissions generated in an afternoon’s drive.

Since memristors are so much more energy-efficient than existing computing systems, they can potentially pack huge amounts of computing power into hand-held devices, removing the need to be connected to the Internet.

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Researchers unveil electronics that mimic the human brain in efficient learning

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Researchers unveil electronics that mimic the human brain in efficient learning

A graphic depiction of protein nanowires (green) harvested from microbe Geobacter (orange) facilitate the electronic memristor device (silver) to function with biological voltages, emulating the neuronal components (blue junctions) in a brain. Credit: UMass Amherst/Yao lab

Only 10 years ago, scientists working on what they hoped would open a new frontier of neuromorphic computing could only dream of a device using miniature tools called memristors that would function/operate like real brain synapses.

But now a team at the University of Massachusetts Amherst has discovered, while on their way to better understanding protein nanowires, how to use these biological, electricity conducting filaments to make a neuromorphic memristor, or “memory transistor,” device. It runs extremely efficiently on very low power, as brains do, to carry signals between neurons. Details are in Nature Communications.

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