A nonlinear and statistical physics approach to machine learning electronic hardware

As the uses of machine learning continue to grow in science and industry, there is a need to reduce power and increase processing speed. As has been done before, hardware co-processors can take over some tasks. We present research developing novel machine learning hardware that relies on a large network of nonlinear electronic nodes to instantiate a reservoir computer. We characterize the behaviors of these networks and find a critical point as we adjust their sensitivity. Moreover, we find that their machine learning performance, in terms of accuracy, depends on the sensitivity of the network.