  Neuromorphic computing systems overcome the limitations of traditional von
Neumann computing architectures. These computing systems can be further
improved upon by using emerging technologies that are more efficient than CMOS
for neural computation. Recent research has demonstrated memristors and
spintronic devices in various neural network designs boost efficiency and
speed. This paper presents a biologically inspired fully spintronic neuron used
in a fully spintronic Hopfield RNN. The network is used to solve tasks, and the
results are compared against those of current Hopfield neuromorphic
architectures which use emerging technologies.
