Reservoir Computing is a novel computing paradigm which uses a nonlinear recurrent dynamical system to carry out information processing. Here we will present an optoelectronic and an all-optical implementation of Reservoir Computers based on an architecture with a single nonlinear node and a delay loop. Moreover we will present an optical analogue readout which makes our reservoir computer a potential standalone solution after training. Our works show that, within the Reservoir Computing paradigm, all-optical computing with state-of-the-art performance is possible as a self-contained solution.

Reservoir computing is a new, powerful and flexible machine learning technique that is easily implemented in hardware. Recently, by using a time-multiplexed architecture, hardware reservoir computers have reached performance comparable to digital implementations. Operating speeds allowing for real time information operation have been reached using optoelectronic systems. At present the main performance bottleneck is the readout layer which uses slow, digital postprocessing. We have designed an analog readout suitable for time-multiplexed optoelectronic reservoir computers, capable of working in real time. The readout has been built and tested experimentally on a standard benchmark task. Its performance is better than non-reservoir methods, with ample room for further improvement. The present work thereby overcomes one of the major limitations for the future development of hardware reservoir computers.