Photonic reservoir computer with all-optical reservoir

A photonic reservoir computer (RC) leverages optical phenomena to implement multiplication by large pseudo-random matrices used by reservoir computers to perform complex machine learning tasks. Here we show that the equations for propagation around a multimode (MM) ring resonator can be cast exactly in the standard RC form with speckle mixing performing the matrix multiplication, an optical nonlinearity, and optical feedback. The hyperparameters are the outcoupling efficiency, the nonlinearity saturation level, and the input bias. The MM ring geometry reduces the sampling rate of backend ADCs by the number of neurons compared to single mode rings and removes the costly optical-to-electrical conversions required at each time step in the arrays. Simulations show a ring using a strongly guiding 50-m planar waveguide gives 200 neurons and excellent predictions and classifications of Mackey-Glass waveforms, while a weakly guiding MM 200-m diameter fiber gives about 4,000 neurons and excellent predictions of chaotic solutions of the Kuramoto-Sivashinsky equation. We perform several simulations of both systems to demonstrate the spatial sampling requirements for the output speckle patterns and that these ring resonator RCs are not excessively sensitive to tuning of the hyperparameters. Finally, we propose designs implementing the system as a chip-scale device or with discrete components and a MM optical fiber.