A Time-shared Photonic Reservoir Computer for Big Data Analytics

TL;DR

This paper introduces a novel photonic reservoir computing system using nanophotonics components, enabling high-speed, parallel processing for big data analytics with improved accuracy over traditional digital methods.

Contribution

It presents the first integrated time-division multiplexed photonic reservoir computer capable of large-scale, parallel machine learning tasks.

Findings

Demonstrates high speed and accuracy in spoken digit recognition

Achieves effective channel equalization and time-series prediction

Outperforms traditional digital implementations in experiments

Abstract

Information processing has reached the era of big data. Big data challenges are difficult to address with traditional Von Neumann or Turing approach. Hence implementation of new computational techniques is highly essential. Nanophotonics with its remarkable speed and multiplexing capability is a promising candidate for such implementations. This paper proposes a novel photonic computing system made-up of Mach-Zehnder interferometer and an optical fiber spool to emulate a powerful machine learning technique called reservoir computing. The proposed system is also integrated with a time-division-multiplexing circuit to facilitate parallel computation of multiple tasks which is first of its kind. The proposed design performs large-scale tasks like spoken digit recognition, channel equalization, and time-series prediction. Experimental results with standard photonic simulator demonstrate significant performance in terms of speed and accuracy compared to state of the art digital and software implementations.