Enabling optical extreme learning machines with nonlinear optics

Nuno A. Silva; Vicente V. Rocha; Tiago D. Ferreira

doi:10.1117/12.3022338

18 June 2024 Enabling optical extreme learning machines with nonlinear optics

Nuno A. Silva, Vicente V. Rocha, Tiago D. Ferreira

Proceedings Volume 13017, Machine Learning in Photonics; 1301716 (2024) https://doi.org/10.1117/12.3022338
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Conference Poster

Abstract

This communication explores an optical extreme learning architecture to unravel the impact of using a nonlinear optical media as an activation layer. Our analysis encloses the evaluation of multiple parameters, with special emphasis on the efficiency of the training process, the dimensionality of the output space, and computing performance across tasks associated with the classification in low-dimensionality input feature spaces. The results enclosed provide evidence of the importance of the nonlinear media as a building block of an optical extreme learning machine, effectively increasing the size of the output space, the accuracy, and the generalization performances. These findings may constitute a step to support future research on the field, specifically targeting the development of robust general-purpose all-optical hardware to a full-stack integration with optical sensing devices toward edge computing solutions.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nuno A. Silva, Vicente V. Rocha, and Tiago D. Ferreira "Enabling optical extreme learning machines with nonlinear optics", Proc. SPIE 13017, Machine Learning in Photonics, 1301716 (18 June 2024); https://doi.org/10.1117/12.3022338

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