Deep photonic reservoir computing recurrent network

TL;DR

This paper introduces a deep photonic reservoir computing architecture with multiple optical layers, demonstrating enhanced capability for complex tasks and real-world optical fiber signal equalization.

Contribution

The work presents the first deep photonic reservoir computing network with cascaded optical layers, avoiding optical-electrical conversion, and demonstrates its application in fiber communication.

Findings

Deep PRC has 4 hidden layers and 320 neurons.

Deep PRC effectively compensates fiber nonlinearity.

All-optical inter-layer connections enhance processing capabilities.

Abstract

Deep neural networks usually process information through multiple hidden layers. However, most hardware reservoir computing recurrent networks only have one hidden reservoir layer, which significantly limits the capability of solving real-world complex tasks. Here we show a deep photonic reservoir computing (PRC) architecture, which is constructed by cascading injection-locked semiconductor lasers. In particular, the connection between successive hidden layers is all optical, without any optical-electrical conversion or analog-digital conversion. The proof of concept is demonstrated on a PRC consisting of 4 hidden layers and 320 interconnected neurons. In addition, we apply the deep PRC in the real-world signal equalization of an optical fiber communication system. It is found that the deep PRC owns strong ability to compensate the nonlinearity of fibers.