학술논문

Programmable Tanh- and ELU-Based Photonic Neurons in Optics-Informed Neural Networks
Document Type
Periodical
Source
Journal of Lightwave Technology J. Lightwave Technol. Lightwave Technology, Journal of. 42(10):3652-3660 May, 2024
Subject
Communication, Networking and Broadcast Technologies
Photonics and Electrooptics
Nonlinear optics
Photonics
Neurons
Training
Integrated circuits
Artificial neural networks
Adaptive optics
Noise aware training
nonlinear activation function
optical neural networks
opto-electronic activation function
Language
ISSN
0733-8724
1558-2213
Abstract
We demonstrate an integrated opto-electronic (ΟΕ) device that can be programmed to provide a set of nonlinear activation functions (AFs) and present its operation within programmable tanh- and ELU-based photonic neurons at line rates up to 10 GBd. The OE activation module provides a set of well-known activation functions that are typically used in DL training models, including the tanh-, ELU- and inverted ELU-like functions. Its performance is experimentally evaluated when incorporated in a 4-input wavelength division multiplexed (WDM) photonic neuron and operating with non-deterministic data patterns, providing “noisy” tanh, ELU and inverted ELU AFs with an error-distribution that has in all cases a standard deviation of