CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume PC12655 > Article
Presentation
28 September 2023 Fundamental Properties of Optical Diffractive Neural Networks Through Co-Design
Francois Leonard, Elliot Fuller, Corinne Teeter, Craig Vineyard
Author Affiliations +
Proceedings Volume PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; PC1265516 (2023) https://doi.org/10.1117/12.2677537
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States
Abstract
Optical Diffractive Neural Networks (ODNNs) have emerged as a new class of AI systems that hold promise for fast and low energy classification of scenes. While these systems resemble electronic neural networks, they also have important differences because they need to satisfy constraints imposed by physical laws of light propagation and light-matter interactions. This brings a number of interesting fundamental questions regarding the ultimate performance that can be achieved, the optimal structure of materials, and even how effectively they can be trained. In this presentation, we will present our efforts to address these questions. In particular, we will discuss how co-design of the diffractive material, the system architecture, and the training algorithms is essential to achieve the best performance and also reveal underlying properties. For example, universal scaling of the performance emerges which differs from traditional electronic NNs. We will also discuss how the properties of the systems differs for coherent and incoherent light. Finally, the role of depth will also be addressed.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Francois Leonard, Elliot Fuller, Corinne Teeter, and Craig Vineyard "Fundamental Properties of Optical Diffractive Neural Networks Through Co-Design", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC1265516 (28 September 2023); https://doi.org/10.1117/12.2677537
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 PRESENTATION
WATCH
PRESENTATION SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
KEYWORDS
Neural networks
Geometrical optics
Optical properties
Education and training
Materials properties
Artificial intelligence
Classification systems
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top