Demonstrating delay-based reservoir computing using a compact photonic integrated chip.

Krishan Harkhoe,Guy Verschaffelt,Guy Van Der Sande,Peter Bienstman,Andrew Katumba

doi:10.1364/oe.382556

Abstract

Photonic delay-based reservoir computing (RC) has gained considerable attention lately, as it allows for simple technological implementations of the RC concept that can operate at high speed. In this paper, we discuss a practical, compact and robust implementation of photonic delay-based RC, by integrating a laser and a 5.4 cm delay line on an InP photonic integrated circuit. We demonstrate the operation of this chip with 23 nodes at a speed of 0.87 GSa/s, showing performances that is similar to previous non-integrated delay-based setups. We also investigate two other post-processing methods to obtain more nodes in the output layer. We show that these methods improve the performance drastically, without compromising the computation speed.

Highlights

The concept of reservoir computing (RC), a paradigm within neuromorphic computing, offers a framework to exploit the transient dynamics within a recurrent neural network for performing useful computation
Photonic delay-based reservoir computing (RC) has gained considerable attention lately, as it allows for simple technological implementations of the RC concept that can operate at high speed
Some examples include a network of semiconductor optical amplifiers [2,3], an integrated passive silicon circuit forming a very complex and random interferometer, with nonlinearity introduced in the readout stage [4] and a semiconductor laser network based on diffractive coupling [5]

Summary

Introduction

The concept of reservoir computing (RC), a paradigm within neuromorphic computing, offers a framework to exploit the transient dynamics within a recurrent neural network for performing useful computation. It has been demonstrated to have state-of-the-art performance for a range of tasks that are notoriously hard to solve by algorithmic approaches, e.g., speech and pattern recognition and nonlinear control. RC simplifies the training procedure for recurrent neural networks, by keeping the neural network fixed and relying on a trained output layer that consists of a linear combination of network states to generate the desired output signals. The fixed network is called the reservoir and can be any dynamical system with a high dimensional state space. Due to this simplification, RC rekindled neuromorphic computing activities in photonics. Some examples include a network of semiconductor optical amplifiers [2,3], an integrated passive silicon circuit forming a very complex and random interferometer, with nonlinearity introduced in the readout stage [4] and a semiconductor laser network based on diffractive coupling [5]

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