  We present a general hardware framework for building networks that directly
implement Reservoir Computing, a popular software method for implementing and
training Recurrent Neural Networks and are particularly suited for temporal
inferencing and pattern recognition. We provide a specific example of a
candidate hardware unit based on a combination of soft-magnets, spin-orbit
materials and CMOS transistors that can implement these networks. Efficient non
von-Neumann hardware implementation of reservoir computers can open up a
pathway for integration of temporal Neural Networks in a wide variety of
emerging systems such as Internet of Things (IoTs), industrial controls, bio-
and photo-sensors, and self-driving automotives.
