  Neural networks have revolutionized the area of artificial intelligence and
introduced transformative applications to almost every scientific field and
industry. However, this success comes at a great price; the energy requirements
for training advanced models are unsustainable. One promising way to address
this pressing issue is by developing low-energy neuromorphic hardware that
directly supports the algorithm's requirements. The intrinsic non-volatility,
non-linearity, and memory of spintronic devices make them appealing candidates
for neuromorphic devices. Here we focus on the reservoir computing paradigm, a
recurrent network with a simple training algorithm suitable for computation
with spintronic devices since they can provide the properties of non-linearity
and memory. We review technologies and methods for developing neuromorphic
spintronic devices and conclude with critical open issues to address before
such devices become widely used.
