Project Description

Machine learning techniques, particularly those based on artificial neural networks (ANNs), have significantly advanced fields such as computer vision and natural language processing, as well as fundamental studies in physics and materials science. Despite considerable advancements in AI algorithms and hardware optimizations aimed at reducing model sizes and accelerating inference and training processes, most efforts have thus far concentrated on traditional electronic systems, including CPUs, GPUs, FPGAs, and ASICs. These systems, while powerful, consume substantial amounts of energy and are unable to meet the increasing demands of rapidly growing data volumes, especially as we approach the limits of Moore's Law. Optical implementation of AI modules, such as optical neural networks (ONNs), presents a compelling alternative that leverages inherent parallelism, high-speed computation, and potential for low energy consumption. Notably, recent studies show that the energy cost of optical implementations has the potential to be 2–3 orders of magnitude less than that of state-of-the-art CMOS implementations. The wave nature and superposition principle of light enables natural parallel processing, such as performing matrix-vector multiplications in constant time—a stark contrast to the quadratic time complexity of digital electronic processors. Additionally, ONNs can execute complex-valued arithmetic by encoding information in both the phase and magnitude of light, further exploiting the unrivaled speed of light as an information carrier. 

Start Date

03/01/2025

Post Doc Qualifications

We are looking for highly motivated, self-driven and diligent postdoctoral researchers, with experimental research experience in optics, and/or atomic physics, and/or artificial intelligence. 

Co-Advisors

Shengwang Du, dusw@purdue.edu, Elmore Family School of Electrical and Computer Engineering

Chunyi Peng, chunyi@purdue.edu, Department of Computer Science 

Bibliography

[1] Ying Zuo, Chenfeng Cao, Ningping Cao, Xuanying Lai, Bei Zeng, and Shengwang Du, “Optical neural network quantum state tomography,” Advanced Photonics 4, 026004 (2022).
[2] Ying Zuo, Yujun Zhao, You-Chiuan Chen, Shengwang Du, and Junwei Liu, “Scalability of all-optical neural networks based on spatial light modulators,” Phys. Rev. Applied 15, 054034 (2021).
[3] Ying Zuo, Bohan Li, Yujun Zhao, Yue Jiang, You-Chiuan Chen, Peng Chen, Gyu-Boong Jo, Junwei Liu, and Shengwang Du, “All-optical neural network with nonlinear activation functions,” Optica 6, 1132 (2019).