Making AI faster and more energy efficient
University of Texas at Arlington
image: Qilian Liang view more
University of Texas at Arlington researcher has received a three-year grant worth nearly $600,000 from the National Science Foundation to make the technology used with artificial intelligence (AI) faster and more energy efficient so it can be used in real time.
Qilian Liang, professor of electrical engineering, will design deep-learning hardware accelerators through devices, circuits and algorithms to create deep generative AI models with simpler design and architecture. Deep-generative AI uses statistics and probability to produce scalable models of complex data, including images, text and data. Liang’s research is expected to generate orders of magnitude improvements in energy use and speed.
Chenyun Pan, assistant professor of electrical engineering, is co-principal investigator on the project
“We will look at architecture, hardware and software to make the AI technology process much faster so it can be implemented in real time and increase its energy efficiency,” Liang said. “Beyond the obvious computing applications, this technology could also make it into the field in robots, autonomous driving and even the process of creating news releases in real time.”
Liang will simplify the architecture used to design hardware to increase computational speed. He will also create an algorithm to determine if AI implementation can cost less and will design more efficient circuits and hardware to save money and allow faster computing.
The team will focus on three types of deep-generative models:
“As AI technology advances, the need for it to be faster and more energy efficient becomes greater,” said Diana Huffaker, chair of the Electrical Engineering Department. “Dr. Liang’s work will enable greater innovation in the future by removing some of the current limitations on this technology.”
Liang joined UTA in 2002. He was named an Institute of Electrical and Electronics Engineers fellow in 2016 because of his contributions to computational intelligence.
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Herb Booth University of Texas at Arlington hbooth@uta.edu Office: 817-272-7075
University of Texas at Arlington
Copyright © 2022 by the American Association for the Advancement of Science (AAAS)
Copyright © 2022 by the American Association for the Advancement of Science (AAAS)