Using multidimensional speckle dynamics for high-speed, large-scale, parallel photonic computing

TL;DR

This paper introduces a novel photonic computing approach using multidimensional speckle dynamics in multimode fibers, enabling high-speed, parallel processing for machine learning tasks at gigasample rates.

Contribution

It demonstrates a new method leveraging speckle-based high-dimensional nonlinear mapping in multimode fibers for fast, parallel photonic computing and multitasking capabilities.

Findings

Achieved chaotic time-series prediction at 12.5 Gigasamples/sec.

Demonstrated multitasking within a single multimode fiber system.

Proposed a scalable, high-speed photonic computing platform.

Abstract

The recent rapid increase in demand for data processing has resulted in the need for novel machine learning concepts and hardware. Physical reservoir computing and an extreme learning machine are novel computing paradigms based on physical systems themselves, where the high dimensionality and nonlinearity play a crucial role in the information processing. Herein, we propose the use of multidimensional speckle dynamics in multimode fibers for information processing, where input information is mapped into the space, frequency, and time domains by an optical phase modulation technique. The speckle-based mapping of the input information is high-dimensional and nonlinear and can be realized at the speed of light; thus, nonlinear time-dependent information processing can successfully be achieved at fast rates when applying a reservoir-computing-like-approach. As a proof-of-concept, we experimentally demonstrate chaotic time-series prediction at input rates of 12.5 Gigasamples per second. Moreover, we show that owing to the passivity of multimode fibers, multiple tasks can be simultaneously processed within a single system, i.e., multitasking. These results offer a novel approach toward realizing parallel, high-speed, and large-scale photonic computing.