Low-cost hardware implementation of Reservoir Computers

The hardware implementation of massive Recurrent Neural Networks to efficiently perform time dependent signal processing is an active field of research. In this work we review the basic principles of stochastic logic and its application to the hardware implementation of Neural Networks. In particular, we focus on the implementation of the recently introduced Reservoir Computer architecture. We show the functionality and low hardware resources used to implement the Reservoir Computer by synthesizing a network performing a mathematical regression.