BRAIN INSPIRED COMPUTING: THE EXTRAORDINARY VOYAGES IN KNOWN AND UNKNOWN WORLDS
Human brain is the most sophisticated organ that nature ever builds. Building a machine that can function like a human brain, indubitably, is the ultimate dream of a computer architect. Although we have not yet fully understood the working mechanism of human brains, the part that we have learned in past seventy years already guided us to many remarkable successes in computing applications, e.g., artificial neural network and machine learning. Inspired by the working mechanism of human brain, neuromorphic system naturally possesses a massively parallel architecture with closely coupled memory, offering a great opportunity to break the "memory wall" in von Neumann architecture. The talk will start with a background introduction of neuromorphic computing, followed by examples of hardware acceleration schemes of learning and neural network algorithms and memristor-based computing engine. I will also share our prospects on the future technology challenges and advances of neuromorphic computing.
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- Date: 24 Oct 2019
- Time: 07:00 PM to 08:00 PM
- All times are (GMT-05:00) US/Eastern
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- 800 Lancaster Ave.
- Villanova University
- Villanova, Pennsylvania
- United States 19085
- Building: Tolentine Hall
- Room Number: 215
- Click here for Map
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- Co-sponsored by VILLANOVA CENTER FOR ANALYTICS OF DYNAMIC SYSTEMS
- Starts 12 September 2019 12:01 AM
- Ends 24 October 2019 07:00 PM
- All times are (GMT-05:00) US/Eastern
- No Admission Charge
Speakers
Hai "Helen" Li of Center for Computational Evolutionary Intelligence, Duke University
BRAIN INSPIRED COMPUTING: THE EXTRAORDINARY VOYAGES IN KNOWN AND UNKNOWN WORLDS
Human brain is the most sophisticated organ that nature ever builds. Building a machine that can function like a human brain, indubitably, is the ultimate dream of a computer architect. Although we have not yet fully understood the working mechanism of human brains, the part that we have learned in past seventy years already guided us to many remarkable successes in computing applications, e.g., artificial neural network and machine learning. Inspired by the working mechanism of human brain, neuromorphic system naturally possesses a massively parallel architecture with closely coupled memory, offering a great opportunity to break the "memory wall" in von Neumann architecture. The talk will start with a background introduction of neuromorphic computing, followed by examples of hardware acceleration schemes of learning and neural network algorithms and memristor-based computing engine. I will also share our prospects on the future technology challenges and advances of neuromorphic computing.
Biography:
Dr. Hai “Helen” Li is Clare Boothe Luce Associate Professor with the Department of Electrical and Computer Engineering at Duke University. She received her B.S and M.S. from Tsinghua University and Ph.D. from Purdue University. At Duke, she co-directs Duke University Center for Computational Evolutionary Intelligence. Her research interests include machine learning acceleration and security, neuromorphic circuit and system for brain-inspired computing, conventional and emerging memory design and architecture, and software and hardware co-design. She received the NSF CAREER Award (2012), the DARPA Young Faculty Award (2013), TUM-IAS Hans Fisher Fellowship from Germany (2017), seven best paper awards and another eight best paper nominations. Dr. Li is a fellow of IEEE and a distinguished member of ACM. For more information, please see her webpage at http://cei.pratt.duke.edu/.
Email:
Address:701 W. Main St., Suite 400, Duke Univeristy, Durham, North Carolina, United States, 27708