Increasing the nonlinear computational capacity of a spatial photonic reservoir computing system

Ian Bauwens; Krishan Harkhoe; Emmanuel Gooskens; Peter Bienstman; Guy Verschaffelt; Guy Van der Sande

doi:10.1117/12.3022352

18 June 2024 Increasing the nonlinear computational capacity of a spatial photonic reservoir computing system

Ian Bauwens, Krishan Harkhoe, Emmanuel Gooskens, Peter Bienstman, Guy Verschaffelt, Guy Van der Sande

Proceedings Volume 13017, Machine Learning in Photonics; 1301717 (2024) https://doi.org/10.1117/12.3022352
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Conference Poster

Abstract

Photonic reservoir computing is a neuromorphic computing framework which has been successfully used for solving various difficult and time-consuming problems. Due to its photonic nature, it offers many potential advantages such as a low-power consumption and fast processing speed. In this work, we aim to improve an already well-established design of a passive spatially distributed photonic reservoir computer, consisting of a network of waveguides connected via optical splitters and combiners. This spatially distributed architecture¹ has shown good performance on a 5-bit header recognition and an isolated spoken digit recognition task. However, this design only incorporates its nonlinearity at the photodiode in its read-out layer and is susceptible to losses within the network. Inspired by the delay-based approach to implement reservoir computing, we opt here for adding extra nonlinearity into the system to increase its nonlinear computational capacity. This is achieved by adding a single semiconductor laser as active component in an external optical delay line: light from the spatial reservoir is injected in a laser, and the optical output of the laser is then fed back to an input port of the spatial reservoir. Based on numerical simulations, we show that the nonlinear computational capacity is significantly increased by adding the feedback loop. This ultimately confirms that adding the active component can be useful for solving more complex tasks.

Citation Download Citation

Ian Bauwens, Krishan Harkhoe, Emmanuel Gooskens, Peter Bienstman, Guy Verschaffelt, and Guy Van der Sande "Increasing the nonlinear computational capacity of a spatial photonic reservoir computing system", Proc. SPIE 13017, Machine Learning in Photonics, 1301717 (18 June 2024); https://doi.org/10.1117/12.3022352

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