Jason K. Eshraghian of Department of Electrical and Computer Engineering, University of California, Santa Cruz.

Abstract: The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons offer a glimpse at what the future of deep learning might look like. Our brains are constantly adapting, our neurons processing all that we know, mistakes we’ve made, failed predictions—all working to anticipate what will happen next with incredible speed. Our brains are also amazingly efficient. Training large-scale neural networks can cost more than $100 million in energy expense, yet the human brain does remarkably well on a power budget of 20 watts.

We can apply the computational principles that underpin the brain, and use them to engineer more efficient systems that adapt to ever changing environments. There is an interplay between neural inspired algorithms, how they can be deployed on low-power microelectronics, and how the brain provides a blueprint for this process.

Biography:

Neuromorphic Computing Group

Brain-Inspired Systems at UC Santa C

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 2016, 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 of the Neural Systems and Applications Technical Committee and as an Associate Editor for APL Machine Learning.

He was awarded the 2023 IEEE Transactions on Circuits and Systems Darlington Best Paper Award, the 2019 IEEE Very Large Scale Integration Systems Best Paper Award, the Best Paper Award at the 2019 IEEE Artificial Intelligence Circuits and Systems Conference, and the Best Live Demonstration Award at the 2020 IEEE International Conference on Electronics Circuits and Systems for his work in neuromorphic computing. He is the recipient of a Fulbright Fellowship (Australian-American Fulbright Commission), 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 snnTorch, a widely used Python library used to train and model spiking neural networks.

Address:Colorado, United States