  Echo-State Networks and Reservoir Computing have been studied for more than a
decade. They provide a simpler yet powerful alternative to Recurrent Neural
Networks, every internal weight is fixed and only the last linear layer is
trained. They involve many multiplications by dense random matrices. Very large
networks are difficult to obtain, as the complexity scales quadratically both
in time and memory. Here, we present a novel optical implementation of
Echo-State Networks using light-scattering media and a Digital Micromirror
Device. As a proof of concept, binary networks have been successfully trained
to predict the chaotic Mackey-Glass time series. This new method is fast, power
efficient and easily scalable to very large networks.
