Abstract: Neuromorphic computing such as SNN leads to a new area of AI system architecture, impacting both hardware devices and software algorithms. Professor Eshraghian’s research explores possibilities of using ideas from Neuroscience to implement more efficient AI Hardware and Software.
Bio: Jason K. Eshraghian is an Assistant Professor with the Department of Electrical and Computer Engineering, University of California, Santa Cruz. He received the Bachelor of Engineering (Electrical and Electronic) and the Bachelor of Laws degrees from The University of Western Australia, WA, Australia, in 2017, where he also received the Ph.D. Degree in 2019. From 2019 to 2022, he was a Post-Doctoral Research Fellow at the University of Michigan, MI, USA. He serves as the Secretary-Elect of the Neural Systems and Applications Technical Committee. He was was awarded the (https://ieee-cas.org/paper-award/outstanding-paper-awards/transactions-very-large-scale-integration-systems-tvsli-best), the Best Paper Award at the 2019 IEEE Artificial Intelligence Circuits and Systems Conference, and the (https://ieee-cas.org/files/ieeecass/2022-06/Women_in_Circuits_and_Systems_WiCAS_and_Young_Professionals_YP_at_ICECS_2020_CAS_Society_News%20%281%29_0.pdf) for his work in neuromorphic computing. He is the recipient of a Fulbright Fellowship (Australian-American Fulbright Comission), a Forrest Research Fellowship (Forrest Research Foundation), and the Endeavour Research Fellowship (Australian Government). His research interests include neuromorphic computing, spiking neural networks, and memory circuits, and he is the developer of (https://snntorch.readthedocs.io/), a widely used Python library used to train and model spiking neural networks.
Co-sponsored by: CH06184 – Santa Clara Valley Section Chapter,SSC37
Cupertino, California, United States, 95014
From Neuroscience to Efficient AI Software and Hardware
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