  We propose an analog implementation of the transcendental activation function
leveraging two spin-orbit torque magnetoresistive random-access memory
(SOT-MRAM) devices and a CMOS inverter. The proposed analog neuron circuit
consumes 1.8-27x less power, and occupies 2.5-4931x smaller area, compared to
the state-of-the-art analog and digital implementations. Moreover, the
developed neuron can be readily integrated with memristive crossbars without
requiring any intermediate signal conversion units. The architecture-level
analyses show that a fully-analog in-memory computing (IMC) circuit that use
our SOT-MRAM neuron along with an SOT-MRAM based crossbar can achieve more than
1.1x, 12x, and 13.3x reduction in power, latency, and energy, respectively,
compared to a mixed-signal implementation with analog memristive crossbars and
digital neurons. Finally, through cross-layer analyses, we provide a guide on
how varying the device-level parameters in our neuron can affect the accuracy
of multilayer perceptron (MLP) for MNIST classification.
