  In artificial neural networks, neurons are usually implemented with highly
dissipative CMOS-based operational amplifiers. A more energy-efficient
implementation is a 'spin-neuron' realized with a magneto-tunneling junction
(MTJ) that is switched with a spin-polarized current (representing weighted sum
of input currents) that either delivers a spin transfer torque or induces
domain wall motion in the soft layer of the MTJ. Here, we propose and analyze a
different type of spin-neuron in which the soft layer of the MTJ is switched
with mechanical strain generated by a voltage (representing weighted sum of
input voltages) and term it straintronic spin-neuron. It dissipates orders of
magnitude less energy in threshold operations than the traditional
current-driven spin neuron at 0 K temperature and may even be faster. We have
also studied the room-temperature firing behaviors of both types of spin
neurons and find that thermal noise degrades the performance of both types, but
the current-driven type is degraded much more than the straintronic type if
both are optimized for maximum energy-efficiency. On the other hand, if both
are designed to have the same level of thermal degradation, then the
current-driven version will dissipate orders of magnitude more energy than the
straintronic version. Thus, the straintronic spin neuron is superior to
current-driven spin neurons.
