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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