The rapid progression of technology has led to a huge increase in energy usage to process the massive troves of data generated by devices.
The rapid progression of technology has led to a huge increase in energy usage to process the massive troves of data generated by devices. But researchers in the Cockrell School of Engineering at The University of Texas at Austin have found a way to make the new generation of smart computers more energy efficient.
Traditionally, silicon chips have formed the building blocks of the infrastructure that powers computers. But this research uses magnetic components instead of silicon and discovers new information about how the physics of the magnetic components can cut energy costs and requirements of training algorithms — neural networks that can think like humans and do things like recognize images and patterns.
"Right now, the methods for training your neural networks are very energy-intensive," said Jean Anne Incorvia, an assistant professor in the Cockrell School's Department of Electrical and Computer Engineering. "What our work can do is help reduce the training effort and energy costs."
The researchers' findings were published this week in IOP Nanotechnology. Incorvia led the study with first author and second-year graduate student Can Cui. Incorvia and Cui discovered that spacing magnetic nanowires, acting as artificial neurons, in certain ways naturally increases the ability for the artificial neurons to compete against each other, with the most activated ones winning out. Achieving this effect, known as “lateral inhibition,” traditionally requires extra circuitry within computers, which increases costs and takes more energy and space.
Read more at University of Texas at Austin
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