Fixed-weight learning neural networks on optical hardware

Authors

A. Steven Younger, MSU JVIC-CASE
Emmett Redd, Missouri State UniversityFollow

Abstract

Fixed-weight learning embeds a learning algorithm into the neural network topology, so its learning can take advantage of all speed increases in its operation on optical neural hardware, up to 10,000 x conventional networks. We developed a hardware-in-the-loop Optical Hardware-based Neural Network Test Apparatus. We used the apparatus to research and develop various embedded learning methods; to work out alignment, calibration, and noise reduction methods; study synaptic weight and neural signal encoding; and to test several small fixed-weight learning neural networks.

Department(s)

Physics, Astronomy, and Materials Science
JVIC

Document Type

Conference Proceeding

DOI

https://doi.org/10.1109/IJCNN.2009.5178903

Publication Date

11-18-2009

Recommended Citation

Younger, A. Steven, and Emmett Redd. "Fixed-weight learning neural networks on optical hardware." In 2009 International Joint Conference on Neural Networks, pp. 3457-3463. IEEE, 2009.