  A device based on current-induced spin-orbit torque (SOT) that functions as
an electronic neuron is proposed in this work. The SOT device implements an
artificial neuron's thresholding (transfer) function. In the first step of a
two-step switching scheme, a charge current places the magnetization of a
nano-magnet along the hard-axis i.e. an unstable point for the magnet. In the
second step, the SOT device (neuron) receives a current (from the synapses)
which moves the magnetization from the unstable point to one of the two stable
states. The polarity of the synaptic current encodes the excitatory and
inhibitory nature of the neuron input, and determines the final orientation of
the magnetization. A resistive crossbar array, functioning as synapses,
generates a bipolar current that is a weighted sum of the inputs. The
simulation of a two layer feed-forward Artificial Neural Network (ANN) based on
the SOT electronic neuron shows that it consumes ~3X lower power than a 45nm
digital CMOS implementation, while reaching ~80% accuracy in the classification
of one hundred images of handwritten digits from the MNIST dataset.
