MIT researchers have pushed the speed limits of analog deep learning

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The applications of the recent development are many, including self-driving cars, fraud detection, and medical image analysis.

The team further indicated that they were able to apply a high voltage to the device without damaging it.

“The action potential in biological cells rises and falls with a timescale of milliseconds since the voltage difference of about 0.1 volt is constrained by the stability of water,” said senior author Ju Li, the Battelle Energy Alliance Professor of Nuclear Science and Engineering and professor of materials science and engineering. “Here we apply up to 10 volts across a special solid glass film of nanoscale thickness that conducts protons, without permanently damaging it.

“Once you have an analog processor, you will no longer be training networks everyone else is working on. You will be training networks with unprecedented complexities that no one else can afford to, and therefore vastly outperform them all. In other words, this is not a faster car, this is a spacecraft,” added lead author and MIT postdoc Murat Onen.

The applications of the new devices are many, including self-driving cars, fraud detection, and medical image analysis.

 

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