Physics-Based Computation for State of the Art Edge Intelligence
Testing in Chen’s lab at Duke shows an AI model that can identify thousands of images transmitted wirelessly with high accuracy in the blink of an eye.
Li is the fourth faculty member in Duke’s Department of Electrical & Computer Engineering to capture the prestigious award.
Hai “Helen” Li, the Marie Foote Reel E’46 Distinguished Professor of Electrical & Computer Engineering and chair of the Department of Electrical and Computer Engineering (ECE), has won the 2025 Edward J. McCluskey Technical Achievement Award for her work in neuromorphic computing and deep-learning acceleration.
The award is given by the Institute of Electrical and Electronics Engineers (IEEE) Computer Society for outstanding and innovative contributions to the fields of computer and information science and engineering or computer technology.
A world-renowned scholar working across hardware and software applications in the development of next-generation computing machinery, Li pursues broad research interests in hardware and software co-design to bridge the gap between the hardware and deep learning communities. Rather than building general processors not optimized for any specific task, co-design puts hardware engineers and software designers together to create new architectures tailored to fulfill specific needs. Depending on the task at hand, for example, this could make the operation simpler and more power-efficient or give it more power and higher performance.
She is known for her pioneering research in neuromorphic computing systems—next-generation computer hardware designs based on the human brain. A leader in her field, Li possesses a deep knowledge of machine learning techniques and applications. She and her students have won numerous awards and recognitions, including nine best-paper awards.
Li joins Yiran Chen (2022), Krishnendu Chakrabarty (2015) and Kishor Trivedi (2008) as previous winners of the award from Duke ECE.
Testing in Chen’s lab at Duke shows an AI model that can identify thousands of images transmitted wirelessly with high accuracy in the blink of an eye.
Duke researchers have shown that large AI model weights can be smartly embedded in the form of radio waves delivered over the air between devices and nearby base stations, opening a path to energy-efficient edge AI without the usual cost in energy, speed or size.
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