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Lecture | Computing, Environment and Life Sciences

Evolutionary Optimization for Neuromorphic Systems

AI Distinguished Lecture

Abstract: Effectively leveraging the characteristics and capabilities of neuromorphic computing systems continues to be a challenge in the field, especially as new hardware, devices, and materials are being developed and applied to neuromorphic implementations.

In this talk, I will overview the use of evolutionary optimization to design spiking neural networks for neuromorphic deployment. I will specifically highlight several co-design efforts with several different types of devices and implementations, including memristors and optoelectronic devices, as well as real-world applications, such as internal combustion engine control and autonomous race car control. I will discuss the advantages of using evolutionary optimization to design spiking neural networks for neuromorphic systems, including the ability to perform multi-objective optimization for energy efficiency and resiliency.

Bio: Catherine (Katie) Schuman is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee (UT). She received her Ph.D. in Computer Science from UT.