Abstract: I will overview our work on analog neural networks based on photonics and other controllable physical systems. In particular, I will discuss why neural networks may serve as an ideal computational model that will enable us to harness the computational power of analog stochastic physical systems in a robust and scalable fashion. I will utilize photonic neural networks as a practical example to demonstrate their robust operation in low-optical-energy regimes, which are typically constrained by quantum noise. Our experimental results indicate that photonic hardware offers a better energy scaling law than electronic for large-scale linear operations. This advantage is particularly significant for the scalability of modern foundational AI models, such as Transformers. Finally, I will show how nonlinear photonic neural networks may also help to enhance computational sensing for a diversity of applications, ranging from autonomous system control to high-throughput biomedical assays.
Pre-seminar snacks will be served in CoorsTek 150 from 3:30-4:00pm; lecture will take place in CTLM102.