  Non-Boolean computing based on emerging post-CMOS technologies can
potentially pave the way for low-power neural computing platforms. However,
existing work on such emerging neuromorphic architectures have either focused
on solely mimicking the neuron, or the synapse functionality. While memristive
devices have been proposed to emulate biological synapses, spintronic devices
have proved to be efficient at performing the thresholding operation of the
neuron at ultra-low currents. In this work, we propose an All-Spin Artificial
Neural Network where a single spintronic device acts as the basic building
block of the system. The device offers a direct mapping to synapse and neuron
functionalities in the brain while inter-layer network communication is
accomplished via CMOS transistors. To the best of our knowledge, this is the
first demonstration of a neural architecture where a single nanoelectronic
device is able to mimic both neurons and synapses. The ultra-low voltage
operation of low resistance magneto-metallic neurons enables the low-voltage
operation of the array of spintronic synapses, thereby leading to ultra-low
power neural architectures. Device-level simulations, calibrated to
experimental results, was used to drive the circuit and system level
simulations of the neural network for a standard pattern recognition problem.
Simulation studies indicate energy savings by ~ 100x in comparison to a
corresponding digital/ analog CMOS neuron implementation.
