Machine Learning in Science presents:
“Neuromorphic and energy-efficient ML”
Dr. Paul Kirkland (University of Strathclyde, Neuromorphic Sensor Signal Processing Lab)
Title: “Bridging Minds and Machines: Unleashing Neuromorphic Engineering for Energy-Efficient, Adaptive Machine Learning”
Abstract: In 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.
Dr. Giulia Marcucci (University of Glasgow, Extreme Light Group)
Title: “Bridging Minds and Machines: Unleashing Neuromorphic Engineering for Energy-Efficient, Adaptive Machine Learning”
Abstract: In 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.
