"ENERGY EFFICIENT NEUROMORPHIC LEARNING AND INFERENCE AT NANOSCALE"
Speaker: Gert Cauwenberghs - Univ. of California, San Diego, La Jolla, CA
Organizer: Gi-Joon Nam - IEEE CEDA & IBM Research, Yorktown Heights, NY
Tuesday, June 26, 12:00pm - 1:30pm | Room 3003
Learning and adaptation are key to natural and artificial intelligence in complex and variable environments. Neural computation and communication in the brain are partitioned into the grey matter of dense local synaptic connectivity in tightly knit neuronal networks, and the white matter of sparse long-range connectivity over axonal fiber bundles across distant brain regions. This exquisite distributed multiscale organization provides inspiration to the design of scalable neuromorphic systems for deep learning and inference, with hierarchical address event-routing of neural spike events and multiscale synaptic connectivity and plasticity, and their efficient implementation in silicon low-power mixed-signal very-large-scale-integrated circuits. Advances in machine learning and system-on-chip integration have led to the development of massively parallel silicon learning machines with pervasive real-time adaptive intelligence at nanoscale that begin to approach the efficacy and resilience of biological neural systems, and already exceed the nominal energy efficiency of synaptic transmission in the mammalian brain. I will highlight examples of neuromorphic learning systems-on-chips with applications in template-based pattern recognition, vision processing, and human-computer interfaces, and outline emerging scientific directions and engineering challenges in their large-scale deployment.
Biography: Gert Cauwenberghs is Professor of Bioengineering and Co-Director of the Institute for Neural Computation at UC San Diego. He received the Ph.D. in Electrical Engineering from Caltech in 1994, and was previously Professor of Electrical and Computer Engineering at Johns Hopkins University, and Visiting Professor of Brain and Cognitive Science at MIT. His research focuses on neuromorphic engineering, adaptive intelligent systems, neuron-silicon and brain-machine interfaces, and micropower biomedical instrumentation. He is a Fellow of the Institute of Electrical and Electronic Engineers (IEEE) and the American Institute for Medical and Biological Engineering (AIMBE) and was a Francqui Fellow of the Belgian American Educational Foundation. He previously received NSF CAREER, ONR Young Investigator Program, and White House PECASE awards. He served IEEE in a variety of roles including recently as Editor-in-Chief of the IEEE Transactions on Biomedical Circuits and Systems.