View in EDS

Probing a theoretical framework for a photonic extreme learning machine.

Saved in:
Bibliographic Details
Title: Probing a theoretical framework for a photonic extreme learning machine.
Authors: Rocha, Vicente1,2 (AUTHOR), Silva, Duarte3 (AUTHOR), Moreira, Felipe C1 (AUTHOR), Monteiro, Catarina S1 (AUTHOR), Ferreira, Tiago D1 (AUTHOR), Silva, Nuno A1,2 (AUTHOR) nuno.a.silva@inesctec.pt
Source: New Journal of Physics. 2026, Vol. 28 Issue 6, p1-15. 15p.
Subjects: Transfer matrix, Extreme learning machines, Transfer functions, Optical computing, Optical sensors, Quantum coherence, Light propagation
Abstract: The development of computing paradigms alternative to von Neumann architectures has recently fueled significant progress in novel all-optical processing solutions. In this work, we investigate how the coherence properties can be exploited for computing by expanding information onto a higher-dimensional space in the photonic extreme learning machine framework. A theoretical framework is provided based on the transmission matrix formalism, mapping the input plane onto the output camera plane, resulting in the establishment of the connection with complex extreme learning machines and derivation of upper bounds for the hidden space dimensionality as well as the form of the activation functions. Experiments using free-space propagation through a diffusive medium, performed in low-dimensional input space regimes, validate the model and the proposed estimator for the dimensionality. Overall, the framework presented and the findings enclosed have the potential to foster further research in a multitude of directions, from the development of robust general-purpose all-optical hardware to a full-stack integration with optical sensing devices toward edge computing solutions. [ABSTRACT FROM AUTHOR]
Copyright of New Journal of Physics is the property of IOP Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:The development of computing paradigms alternative to von Neumann architectures has recently fueled significant progress in novel all-optical processing solutions. In this work, we investigate how the coherence properties can be exploited for computing by expanding information onto a higher-dimensional space in the photonic extreme learning machine framework. A theoretical framework is provided based on the transmission matrix formalism, mapping the input plane onto the output camera plane, resulting in the establishment of the connection with complex extreme learning machines and derivation of upper bounds for the hidden space dimensionality as well as the form of the activation functions. Experiments using free-space propagation through a diffusive medium, performed in low-dimensional input space regimes, validate the model and the proposed estimator for the dimensionality. Overall, the framework presented and the findings enclosed have the potential to foster further research in a multitude of directions, from the development of robust general-purpose all-optical hardware to a full-stack integration with optical sensing devices toward edge computing solutions. [ABSTRACT FROM AUTHOR]
ISSN:13672630
DOI:10.1088/1367-2630/ae51b5