Bridging minds and machines: unleashing neuromorphic enginerring for energy-efficient, adaptive machine learning
Paul Kirkland
University of StrathclydeIn this talk, we will explore the fascinating world of neuromorphic engineering, a field inspired by the human brain's neural networks. Discover how this innovative approach is revolutionizing the creation of machine learning models, enabling them to operate efficiently on low power, adapt seamlessly to real-world environments, and herald a new era/paradigm in artificial intelligence. Delving into the neural circuits that inspire neuromorphic computing and the limitless possibilities it holds for AI applications.
Towards sustainable AI hardware
Giulia Marcucci
University of Glasgow | School of Physics and AstronomyMy talk explores leveraging nonlinear wave complexity in optics and hydrodynamics to design sustainable AI hardware. Specifically, I will spotlight wave-based reservoir computing, a promising paradigm for in-material computing with significant potential for sustainability.
