Reservoir neuromorphic computing based on spin-orbit coupling in an organic crystal resonator

TL;DR

This paper demonstrates how organic crystal resonators with spin-orbit coupling can significantly enhance reservoir neuromorphic computing by reducing network size and accelerating learning, offering a promising path for efficient photonic AI systems.

Contribution

It introduces a novel organic crystal resonator with spin-orbit coupling that improves reservoir computing performance by reducing network size and increasing processing speed.

Findings

10-fold reduction in network size for complex symbols

3-fold acceleration of learning process

Efficient separation of optical patterns in organic resonators

Abstract

Neuromorphic computing is at the basis of the recent progress in artificial intelligence. But the progress is accompanied with increasing demands in computational resources and power supply. Reservoir neuromorphic computing uses a non-linear physical system to replace a part of a large neural network. The advantages can include reduced power consumption and faster learning. We show that the interference in an organic crystal waveguide resonator leads to efficient separation of optical patterns, allowing a significant reduction of the size of the neural network and an acceleration of the learning process. For more complex symbols, extending the reservoir output dimension thanks to spin-orbit coupling, we achieve a 10-times reduction of the network size and a 3-fold speedup. Our work suggests a general path for the performance improvement of photonic reservoir computing systems.