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A Blog Devoted to UD Innovation, Excellence and Scholarship
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Research & Discovery

A Blog Devoted to UD Innovation, Excellence and Scholarship

Neural networks

by | October 10, 2019

Chengmo Yang

ABOVE: Associate Professor of Electrical and Computer Engineering Chengmo Yang is researching ways to support neural networks in low-power embedded systems in items such as smartphones by using emerging memory devices that can retrieve information even when powered off, and furthermore minimize errors in these emerging devices. | Photo illustration by Joy Smoker and Chengmo Yan

UD research could expand the use of artificial intelligence on internet-of-things hardware

If you have a smart phone with facial recognition, you may have wondered: How does your device learn to recognize your face as opposed to, say, your spouse’s face?

Credit a neural network, a form of artificial intelligence increasingly used in everyday devices. Neural networks are algorithms trained to recognize patterns and continuously improve their ability to do so — just as the human brain does.

In order to be so smart, neural networks require a lot of power, which so far has limited their usefulness in small, battery-powered devices. Have you ever wondered why your smartphone supports face ID but your smartwatch does not? Simply because the watch does not have sufficient power to support it. With a new grant from the National Science Foundation (NSF), University of Delaware Associate Professor of Electrical and Computer Engineering Chengmo Yang is researching ways to support neural networks in low-power embedded systems by using emerging memory devices that can retrieve information even when powered off, and furthermore minimize errors in these emerging devices.

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UD researchers have been recognized recently by the National Institutes of Health, American Political Science Association, TED Fellows program, National Science Foundation, National Academy of Inventors and the Gates Cambridge Scholarship program.

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