Photonic reservoir computer based on frequency multiplexing
This work addresses the need for high-speed, low-footprint analogue computing systems, particularly in photonics, though it is incremental as it builds on existing reservoir computing methods with a new implementation.
The authors tackled the challenge of implementing reservoir computing in photonics by using frequency multiplexing to encode neuron states, achieving processing of 25 neurons at 20 MHz and demonstrating performance on benchmark tasks like channel equalization and time series forecasting.
Reservoir computing is a brain inspired approach for information processing, well suited to analogue implementations. We report a photonic implementation of a reservoir computer that exploits frequency domain multiplexing to encode neuron states. The system processes 25 comb lines simultaneously (i.e. 25 neurons), at a rate of 20 MHz. We illustrate performances on two standard benchmark tasks: channel equalization and time series forecasting. We also demonstrate that frequency multiplexing allows output weights to be implemented in the optical domain, through optical attenuation. We discuss the perspectives for high speed high performance low footprint implementations.