All-optical reservoir computing system based on InGaAsP ring resonators for high-speed identification and optical routing in optical networks

In this paper an all-optical reservoir computing scheme is modeled, that paves an alternative route to photonic high bit rate header identification in optical networks and allow direct processing in the analog domain. The system consists of randomly interconnected InGaAsP micro-ring-resonators, whereas the computation efficiency of the scheme is based on the ultra-fast Kerr effect and two-photon absorption. Validation of the system’s efficiency is confirmed through detailed numerical modeling and two application orientated benchmark tests that consists in the classification of 32bit digital headers, encoded an NRZ optical pulses, with a bitrate of 240Gbps,and the identification of pseudo-analog patters for real time sensing applications in the analog domain.

[1]  Geert Morthier,et al.  Experimental demonstration of reservoir computing on a silicon photonics chip , 2014, Nature Communications.

[2]  Benjamin Schrauwen,et al.  Parallel Reservoir Computing Using Optical Amplifiers , 2011, IEEE Transactions on Neural Networks.

[3]  Dimitris Syvridis,et al.  Micro ring resonators as building blocks for an all-optical high-speed reservoir-computing bit-pattern-recognition system , 2013 .

[4]  Benjamin Schrauwen,et al.  Real-time epileptic seizure detection using reservoir computing , 2009 .

[5]  G De Moor,et al.  Novel time series analysis approach for prediction of dialysis in critically ill patients using echo-state networks , 2009, Critical Care.

[6]  D. Syvridis,et al.  Intraband Crosstalk Properties of Add–Drop Filters Based on Active Microring Resonators , 2007, IEEE Photonics Technology Letters.

[7]  Mario Köppen,et al.  Advances in Neuro-Information Processing, 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part I , 2009, International Conference on Neural Information Processing.

[8]  Pericles A. Mitkas,et al.  Enhancing Agent Intelligence through Evolving Reservoir Networks for Predictions in Power Stock Markets , 2011, ADMI.

[9]  Tao Li,et al.  Online learning for behavior switching in a soft robotic arm , 2013, 2013 IEEE International Conference on Robotics and Automation.

[10]  Henry Markram,et al.  Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.

[11]  L. Appeltant,et al.  Information processing using a single dynamical node as complex system , 2011, Nature communications.