Neuroevolution with genetic algorithms and particle swarm optimization can train simulated optical neural networks to accuracies comparable to gradient-based methods on small classification tasks.
Self-learning photonic signal processor with an optical neural network chip
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Photonic signal processing is essential in the optical communication and optical computing. Numerous photonic signal processors have been proposed, but most of them exhibit limited reconfigurability and automaticity. A feature of fully automatic implementation and intelligent response is highly desirable for the multipurpose photonic signal processors. Here, we report and experimentally demonstrate a fully self-learning and reconfigurable photonic signal processor based on an optical neural network chip. The proposed photonic signal processor is capable of performing various functions including multichannel optical switching, optical multiple-input-multiple-output descrambler and tunable optical filter. All the functions are achieved by complete self-learning. Our demonstration suggests great potential for chip-scale fully programmable optical signal processing with artificial intelligence.
fields
cs.NE 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Efficient training and design of photonic neural network through neuroevolution
Neuroevolution with genetic algorithms and particle swarm optimization can train simulated optical neural networks to accuracies comparable to gradient-based methods on small classification tasks.