A Computer Engineering Journey to Optical Neural Networks: Infrastructure, Algorithms, and Co-design

image of a neural network

Department of Electrical and Computer Engineering

Location: Burchard 714

Speaker: Dr. Cunxi Yu, Assistant Professor, University of Maryland, College Park


Despite the significant progress in customized ML/AI accelerator designs, the Pareto-frontier encompassing performance, energy efficiency, and carbon emissions of digital accelerators remains unchanged due to the reliance on conventional technologies. As an alternative, optical neural networks (ONNs), such as diffractive optical neural networks (DONNs), promise vast improvements in terms of computing speed, power efficiency, and carbon dioxide emissions. Nonetheless, designing and deploying DONNs face critical challenges. These primarily stem from the requirements for domain-specific infrastructure, algorithms, and the hurdles posed by multi-disciplinary domain knowledge in optical physics, fabrication, ML, and co-design.

In this presentation, I will share our journey towards the automatic and agile design of DONNs. We address the challenges of building tangible DONN systems through multi-disciplinary developments encompassing physics, algorithms, co-design, and hands-on prototyping. I will begin by introducing the core concepts of DONNs and the associated design difficulties. Subsequently, I will detail our comprehensive design infrastructure, LightRidge, and the physics-aware hardware-software co-design algorithms that facilitate immediate DONN fabrication and deployment using physical prototypes. I will conclude with recent case studies showcasing the application in complex tasks, like autonomous driving, facilitated by ML-assisted architecture exploration.


Portrait of Cunxi Yu, wearing glasses

Dr. Yu is an Assistant Professor at the University of Maryland, College Park. His research interests focus on novel algorithms, systems, and hardware designs for computing and security. Before joining University of Maryland, Yu was Assistant Professor with University of Utah, and held PostDoc at Cornell University. His work received the Best Paper Award at DAC (2023), Best Paper Nominations at ASP-DAC (2017), TCAD (2018), and 1st place at DAC Security Contest (2017), NSF CAREER Award (2021), and American Physical Society DLS poster award (2022). Yu earned his Ph.D. from UMass Amherst in 2017.