Parallel processing using an optical delay-based reservoir computer

Guy Van der Sande; Romain Modeste Nguimdo; Guy Verschaffelt

doi:10.1117/12.2228005

27 April 2016 Parallel processing using an optical delay-based reservoir computer

Guy Van der Sande, Romain Modeste Nguimdo, Guy Verschaffelt

Proceedings Volume 9894, Nonlinear Optics and its Applications IV; 98941P (2016) https://doi.org/10.1117/12.2228005
Event: SPIE Photonics Europe, 2016, Brussels, Belgium

Abstract

Delay systems subject to delayed optical feedback have recently shown great potential in solving computationally hard tasks. By implementing a neuro-inspired computational scheme relying on the transient response to optical data injection, high processing speeds have been demonstrated. However, reservoir computing systems based on delay dynamics discussed in the literature are designed by coupling many different stand-alone components which lead to bulky, lack of long-term stability, non-monolithic systems. Here we numerically investigate the possibility of implementing reservoir computing schemes based on semiconductor ring lasers. Semiconductor ring lasers are semiconductor lasers where the laser cavity consists of a ring-shaped waveguide. SRLs are highly integrable and scalable, making them ideal candidates for key components in photonic integrated circuits. SRLs can generate light in two counterpropagating directions between which bistability has been demonstrated. We demonstrate that two independent machine learning tasks , even with different nature of inputs with different input data signals can be simultaneously computed using a single photonic nonlinear node relying on the parallelism offered by photonics. We illustrate the performance on simultaneous chaotic time series prediction and a classification of the Nonlinear Channel Equalization. We take advantage of different directional modes to process individual tasks. Each directional mode processes one individual task to mitigate possible crosstalk between the tasks. Our results indicate that prediction/classification with errors comparable to the state-of-the-art performance can be obtained even with noise despite the two tasks being computed simultaneously. We also find that a good performance is obtained for both tasks for a broad range of the parameters. The results are discussed in detail in [Nguimdo et al., IEEE Trans. Neural Netw. Learn. Syst. 26, pp. 3301–3307, 2015]

Citation Download Citation

Guy Van der Sande, Romain Modeste Nguimdo, and Guy Verschaffelt "Parallel processing using an optical delay-based reservoir computer", Proc. SPIE 9894, Nonlinear Optics and its Applications IV, 98941P (27 April 2016); https://doi.org/10.1117/12.2228005

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