We've updated our Privacy Policy to make it clearer how we use your personal data.

We use cookies to provide you with a better experience. You can read our Cookie Policy here.

Advertisement
Optical Neural Networks Close the Gap on Their Electronic Counterparts
News

Optical Neural Networks Close the Gap on Their Electronic Counterparts

Optical Neural Networks Close the Gap on Their Electronic Counterparts
News

Optical Neural Networks Close the Gap on Their Electronic Counterparts

Operation principles of a differential diffractive optical neural network. Since diffractive optical neural networks operate using coherent illumination, phase and/or amplitude channels of the input plane can be used to represent information. Credit: SPIE
Read time:
 

Want a FREE PDF version of This News Story?

Complete the form below and we will email you a PDF version of "Optical Neural Networks Close the Gap on Their Electronic Counterparts"

First Name*
Last Name*
Email Address*
Country*
Company Type*
Job Function*
Would you like to receive further email communication from Technology Networks?

Technology Networks Ltd. needs the contact information you provide to us to contact you about our products and services. You may unsubscribe from these communications at any time. For information on how to unsubscribe, as well as our privacy practices and commitment to protecting your privacy, check out our Privacy Policy

A new paper in Advanced Photonics, an open-access journal co-published by SPIE, the international society for optics and photonics, and Chinese Laser Press (CLP), demonstrates distinct improvements to the inference and generalization performance of diffractive optical neural networks.


One of the key improvements discussed in the paper, "Class-specific differential detection in diffractive optical neural networks improves inference accuracy," incorporates a differential detection scheme combined with a set of parallel-operating diffractive optical networks, where each individual network of this set is specialized to specifically recognize a sub-group of object classes.


According to SPIE Fellow Aydogan Ozcan of the University of California, Los Angeles, and one of the paper's authors, these results "provide a major advancement to bring optical neural network-based low-power and low-latency solutions for various machine-learning applications."


This latest research is a significant advance to Ozcan's optical machine-learning framework: the finessing of this technology is especially significant for recognizing target objects more quickly and with significantly less power than standard computer-based machine learning systems. Ultimately, it may provide major advantages for autonomous vehicles, robotics and various defense-related applications, among others.


These latest systematic advances, in diffractive optical network designs in particular, have the potential to advance the development of next-generation, task-specific, and intelligent computational camera systems.

Reference: Li, J., Mengu, D., Luo, Y., Rivenson, Y., & Ozcan, A. (2019). Class-specific differential detection in diffractive optical neural networks improves inference accuracy. Advanced Photonics, 1(4), 046001. https://doi.org/10.1117/1.AP.1.4.046001


This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.

Advertisement