Making big data processing more energy efficient using magnetic circuits

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.