Cascading photonic reservoirs with deep neural networks increases computational performance

Ian Bauwens; Guy Van der Sande; Peter Bienstman; Guy Verschaffelt

doi:10.1117/12.3017209

18 June 2024 Cascading photonic reservoirs with deep neural networks increases computational performance

Ian Bauwens, Guy Van der Sande, Peter Bienstman, Guy Verschaffelt

Proceedings Volume 13017, Machine Learning in Photonics; 1301710 (2024) https://doi.org/10.1117/12.3017209
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Conference Poster

Abstract

Deep neural networks (DNNs) have been successfully applied to solve complex problems, such as pattern recognition when analyzing big data. To achieve a good computational performance, these networks are often designed such that they contain a large number of trainable parameters. However, by doing so, DNNs are often very energy-intensive and time-consuming to train. In this work, we propose to use a photonic reservoir to preprocess the input data instead of directly injecting it into the DNN. A photonic reservoir consists of a network of many randomly connected nodes which do not need to be trained. It forms an additional layer to the deep neural network and can transform the input data into a state in a higher dimensional state-space. This allows us to reduce the size of the DNN, and the amount of training required for the DNN. We test this assumption using numerical simulations that show that such a photonic reservoir as preprocessor results in an improved performance, shown by a lower test error, for a deep neural network, when tested on the one-step ahead prediction task of the Santa Fe time-series. The performance of the stand-alone DNN is poor on this task, resulting in a high test error. As we also discuss in detail in [Bauwens et al, Frontiers in Physics 10, 1051941 (2022)], we conclude that photonic reservoirs are well-suited as physical preprocessors to deep neural networks for tackling time-dependent tasks due to their fast computation times and low-energy consumption.

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

Citation Download Citation

Ian Bauwens, Guy Van der Sande, Peter Bienstman, and Guy Verschaffelt "Cascading photonic reservoirs with deep neural networks increases computational performance", Proc. SPIE 13017, Machine Learning in Photonics, 1301710 (18 June 2024); https://doi.org/10.1117/12.3017209

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available