Time: 11:30-12:15，Oct.25, 2023，Wednesday
Prof. Gert Cauwenberghs
UC San Diego ,USA
We present neuromorphic and artificially intelligent cognitive computing systems-on-chip implemented in custom silicon compute-in-memory neural and memristive synaptic crossbar array architectures that combine the efficiency of local interconnects with flexibility and sparsity in global interconnects, and that realize a wide class of deeply layered and recurrent neural network topologies with embedded local plasticity for on-line learning, at a fraction of the computational and energy cost of implementation on CPU and GPGPU platforms. Co-optimization across the abstraction layers of hardware and algorithms leverages inherent stochasticity in the physics of synaptic memory devices and neural interface circuits with plasticity in reconfigurable massively parallel architecture towards high system-level accuracy, resilience, and efficiency. Adiabatic energy recycling in charge-mode crossbar arrays permit extreme scaling in energy efficiency, approaching that of synaptic transmission in the mammalian brain.
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 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 a Francqui Fellow of the Belgian American Educational Foundation.