Rapid Time Series Prediction with a Hardware-Based Reservoir Computer

There is considerable interest in the machine learning community in using recurrent neural networks (RNN) for processing time-dependent signals. Many machine learning and artificial intelligence tasks, such as dynamical system modeling, human speech recognition, and natural languageprocessing are intrinsically time-dependent tasks, and thus are more naturally handled within a timedependent, neural-networkframework. Though they have high expressive power, RNNs are difficult to train using gradient-descent-based methods. One approach to efficiently and rapidly train an RNN is known as reservoir computing (RC). In RC, the network isdivided into input nodes, a bulk collection of nodes known as the reservoir, and output nodes, such that the only recurrent links are between reservoir nodes.Training involves only adjusting the weights along links connecting the reservoir to the output nodes and not the recurrent links in the reservoir. Recently, implementations of reservoir computing using dedicatedhardware have achieved much attention, particularlythose based on delay-coupled photonic systems. These devices allow for reservoir computing at extremely high speeds, including the classification of spoken words at a rate of millions of words per second. There is also the potential to form the input and output layersout of optics as well, resulting in an all-optical computational device. Reservoir computing is a neural network approach for processing time-dependent signals that has seen rapid development in recent years. Physical implementations of the technique using optical reservoirs have demonstrated remarkable accuracy and processing speed at benchmark tasks. However, these approaches require an electronic output layer to maintain high performance, which limits their use in tasks such as time-series prediction, where the output is fed back into the reservoir. We present here a reservoir computing scheme that has rapid processing speed both by the reservoir and the output layer.