  Stochastic spiking neural networks based on nanoelectronic spin devices can
be a possible pathway to achieving "brainlike" compact and energy-effcient
cognitive intelligence. The computational model attempt to exploit the
intrinsic device stochasticity of nanoelectronic synaptic or neural components
to perform learning or inference. However, there has been limited analysis on
the scaling effect of stochastic spin devices and its impact on the operation
of such stochastic networks at the system level. This work attempts to explore
the design space and analyze the performance of nanomagnet-based stochastic
neuromorphic computing architectures for magnets with different barrier
heights. We illustrate how the underlying network architecture must be modified
to account for the random telegraphic switching behavior displayed by magnets
with low barrier heights as they are scaled into the superparamagnetic regime.
We perform a device-to-system-level analysis on a deep neural-network
architecture for a digit-recognition problem on the MNIST data set.
