Physics colloquium-“Optical Neural Networks: Neuromorphic Computing and Sensing in the Optical Domain”

Tianyu Wang

Boston University

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.

Bio: Tianyu Wang is an Assistant Professor to the Department of Electrical and Computer Engineering at Boston University. He is interested in developing novel methods for imaging, sensing, and computing by leveraging emerging technologies from photonics and artificial intelligence.

Pre-seminar snacks will be served in CoorsTek 150 from 3:30-4:00pm; lecture will take place in CTLM102.