Building Physical Neural Networks

This lecture explores the challenges of building physical neural networks, focusing on key aspects such as model size, density of connections, and trainability. The instructor discusses the need for depth in networks, long-range connections, and high connection density. The lecture delves into the limitations of physical systems in achieving these requirements and presents innovative solutions using spintronics and neuromorphic computing. The presentation covers topics like spintronics, neuromorphic computing, and the integration of emerging technologies for building complex neural networks.