A Microring as a Reservoir Computing Node: Memory/Nonlinear Tasks and Effect of Input Non-ideality

TL;DR

This paper demonstrates how an optical microresonator can serve as a reservoir computing node, capable of handling memory and nonlinear tasks, with analysis of input non-ideality effects and performance comparison.

Contribution

It introduces a method to use an optical microresonator as a reservoir computing node, analyzing its memory and nonlinear capabilities with input non-ideality considerations.

Findings

Microresonator exhibits up to two bits of memory in linear tasks.

It can solve nonlinear tasks by combining memory and nonlinearity.

Performance comparison with input signal training is essential.

Abstract

The nonlinear response of an optical microresonator is used in a time multiplexed reservoir computing neural network. Within a virtual node approach combined with an offline training through ridge regression, we solved linear and nonlinear logic operations. We analyzed the nonlinearity of the microresonator as a memory between bits and/or as a neural activation function. This is made possible by controlling both the distance between bits subject to the logical operation and the number of bits supplied to the ridge regression. We show that the optical microresonator exhibits up to two bits of memory in linear tasks and that it allows solving nonlinear tasks providing both memory and nonlinearity. Finally, we demonstrate that the virtual node approach always requires a comparison of the reservoir's performance with the results obtained by applying the same training process on the input signal.