Event

ANNOUNCEMENT:  Ph.D. Dissertation Defense

 

NAME: Yingjie Li

Committee:
Prof. Cunxi Yu (Chair)
Prof. Bahar Asgari
Prof. Thomas E. Murphy
Prof. Weilu Gao
Prof. Dinesh Manocha (Dean’s Representative)

Date/time: Tuesday, May 13, 2025 at 12pm – 2pm

Location: AVW 1146

Title: Bridging Light with Deep Learning: Algorithm, Compiler, and Applications

Abstract: Deep neural networks (DNNs) have shown great potential in dealing with
various intelligent tasks. However, conventional neural networks deployed on digital
platforms have intrinsic limitations in their throughput, computation speed, and energy
consumption, especially for the resource-constrained applications. A more scalable,
faster, and energy-efficient method is needed for machine learning development: optical
neural networks (ONNs), where information is encoded on the light signal. Among
ONNs, free-space Diffractive ONNs (DONNs) process information-encoded light signal
using trained passive devices, achieving high system throughput, light-speed
computation, and energy efficiency due to no added energy costs from light
propagation, diffraction, and phase modulation with passive optical devices.
However, there are several critical challenges that limit the development and exploration
of DONNs – (a) Existing physics engines for DONN emulation and training have high
computational demands, and there’s no high performance computing (HPC) optimized
language tailored for ONNs ensuring flexibility and maintainability. (b) There’s a lack of
hardware-software co-design algorithms for practical DONNs from design to fabrication.
(c) The absence of robust emulation frameworks limits the practical applications of
DONNs, as the design and explorations of DONN systems require sufficient
multi-disciplinary domain knowledge, posing critical technical barriers. Finally, (d) the
accessibility of reproducing and advancing DONN research to broader communities is
constrained, which posts great needs in developing an open-sourced design
infrastructure.

Targeting the improvements to the development of DONNs, my dissertation will include
(1) Physics-aware differentiable co-design algorithm designed specifically for DONN
systems, enabling the efficient and accurate system training and design automation; (2)
Physics-aware optical adversary investigations, uncovering the unique optical security
vulnerabilities in optical neural networks and offering insights into adversarial strategies
applicable to other complex-domain systems; (3) An end-to-end agile design framework
LightRidge designed specifically for DONN systems. It integrates with the proposed
co-design algorithms and accurate and high-performance optimized computing kernels
together with user-friendly domain-specific-language support, providing seamless
design-to-deployment workflow for DONN systems and bridging the expertise gap for
cross-disciplinary research; (4) various machine learning applications and explorations
with DONNs including physics-aware multi-task learning, all-optical graph learning, and
all-optical autonomous driving with DONN systems, demonstrating the capability of
DONN systems in real-world applications and enriching the research for DONNs.