Researchers from the Cockrell School of Engineering at The University of Texas at Austin have discovered a new way to increase the energy efficiency of smart computers. This comes during a time when there is an increased need for energy in order to process massive amounts of data, a result of newly developed technology.
Silicon chips are normally used to build the infrastructure that powers computers, but the newly developed system relies on magnetic components instead of silicon. The silicon chips are beginning to reach their limitations, due to things like artificial intelligence, self-driving cars, and 5G and 6G phones. New applications require faster speeds, reduced latency, and light detection, all requiring increased energy. Because of this, alternatives to silicone are being looked at.
By studying the physics of the magnetic components, the researchers found new information about how energy costs can be decreased. They also discovered ways to decrease the requirements of training algorithms, which are neural networks capable of what can you do with a computer science degree patterns and images.
Jean Anne Incorvia is an assistant professor in the Cockrell School’s Department of Electrical and Computer Engineering.
“Right now, the methods for training your neural networks are very energy-intensive,” said Jean Anne Incorvia. “What our work can do is help reduce the training effort and energy costs.”
Silicon chips are normally used to build the infrastructure that powers computers, but the newly developed system relies on magnetic components instead of silicon. The silicon chips are beginning to reach their limitations, due to things like artificial intelligence, self-driving cars, and 5G and 6G phones. New applications require faster speeds, reduced latency, and light detection, all requiring increased energy. Because of this, alternatives to silicone are being looked at.
By studying the physics of the magnetic components, the researchers found new information about how energy costs can be decreased. They also discovered ways to decrease the requirements of training algorithms, which are neural networks capable of what can you do with a computer science degree patterns and images.
Jean Anne Incorvia is an assistant professor in the Cockrell School’s Department of Electrical and Computer Engineering.
“Right now, the methods for training your neural networks are very energy-intensive,” said Jean Anne Incorvia. “What our work can do is help reduce the training effort and energy costs.”
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