  Complementary metal oxide semiconductor (CMOS) devices display volatile
characteristics, and are not well suited for analog applications such as
neuromorphic computing. Spintronic devices, on the other hand, exhibit both
non-volatile and analog features, which are well-suited to neuromorphic
computing. Consequently, these novel devices are at the forefront of
beyond-CMOS artificial intelligence applications. However, a large quantity of
these artificial neuromorphic devices still require the use of CMOS, which
decreases the efficiency of the system. To resolve this, we have previously
proposed a number of artificial neurons and synapses that do not require CMOS
for operation. Although these devices are a significant improvement over
previous renditions, their ability to enable neural network learning and
recognition is limited by their intrinsic activation functions. This work
proposes modifications to these spintronic neurons that enable configuration of
the activation functions through control of the shape of a magnetic domain wall
track. Linear and sigmoidal activation functions are demonstrated in this work,
which can be extended through a similar approach to enable a wide variety of
activation functions.
