  Energy-efficient methods are addressed for leveraging low energy barrier
nanomagnetic devices within neuromorphic architectures. Using a
Magnetoresistive Random Access Memory (MRAM) probabilistic device (p-bit) as
the basis of neuronal structures in Deep Belief Networks (DBNs), the impact of
reducing the Magnetic Tunnel Junction's (MTJ's) energy barrier is assessed and
optimized for the resulting stochasticity present in the learning system. This
can mitigate the process variation sensitivity of stochastic DBNs which
encounter a sharp drop-off when energy barriers exceed near-zero kT. As
evaluated for the MNIST dataset for energy barriers at near-zero kT to 2.0 kT
in increments of 0.5 kT, it is shown that the stability factor changes by 5
orders of magnitude. The self-compensating circuit developed herein provides a
compact, and low complexity approach to mitigating process variation impacts
towards practical implementation and fabrication.
