Fixed-weight learning neural networks on optical hardware
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.
