Reservoir Neuromorphic Computing Based on Spin-Orbit Coupling in an Organic Crystal Resonator
Abstract Neuromorphic computing is at the basis of the recent progress in artificial intelligence, but this progress is accompanied by an increasing demand for computational resources and power supply. Reservoir neuromorphic computing uses a nonlinear 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 in 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-fold reduction of the network size and a 3-fold speedup with respect to using intensity only. Our work suggests a general path for improving the performance of photonic reservoir computing systems.