ECE Ph.D. Student Places in ACM Competition

ECE Ph.D. student Yingji Li placed 2nd in the recent IEEE/ACM SIGDA (Association for Computing Machinery Special Interest Group on Design Automation) student research competition. More than 40 Ph.D. Students submitted papers for the competition, which took place during the IEEE/ACM 2023 International Conference on Computer-Aided Design in San Francisco.

Li’s recent work, titled “Bridging Light with Deep Learning – Compiler, Algorithms, and Exploration”, discusses the limitations on conventional neural networks used on digital platforms, including throughput, computation speed and energy consumption, and the need for instead using optical neural networks with use light signals instead of electrical ones. These diffractive optical neural networks can be more scalable, faster and energy efficient. She also addresses the challenges that can be present in the development and exploration of diffractive optical neural networks.

As a Computer Engineering Ph.D. student, Li is advised by Professor Cunxi Yu. Her research interests include compilation systems for optical neural networks, including physics-aware codesign algorithm and end-to-end prototyping, in addition to Electronic Design Automation (EDA) including machine learning for EDA and differentiable scheduling.

During the summer of 2023, Li completed an internship with NVIDIA, a world leader in AI, data science and high-performance computing. She was selected as a Young Fellow of the Design Automation Conference (DAC) in 2020, 2021, and 2022. She received a Best Paper Presentation at DAC 2023, and was noted an Electrical Engineering & Computer Sciences Rising Star in 2023.

Published November 10, 2023