Photonic delay-based reservoir computers as deep neural network preprocessors

Photonic reservoir computing (RC) has been effectively used for solving various complex problems. Such a reservoir consists of a network of randomly, untrained connected nodes. Doing RC in the photonics domain offers the advantage of high-speed performance, low-energy consumption and the possibility of high inherent parallelism. We propose and numerically investigate to use the output of such a reservoir to preprocess the input data before this data is send to a DNN. The main idea here is to use such a photonic reservoir to transform the input data into a higher dimensional state-space, which could allow the DNN to process the data with increased performance. Based on numerical simulations of delay-based reservoirs using a single-mode semiconductor laser, we show that using such a preprocessing reservoir results in an improved performance of DNNs, and that we do not need to carefully fine-tune the parameters of the preprocessing reservoir.