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Energy-efficient methods are addressed for leveraging low energy barriernanomagnetic devices within neuromorphic architectures. Using aMagnetoresistive Random Access Memory (MRAM) probabilistic device (p-bit) asthe basis of neuronal structures in Deep Belief Networks (DBNs), the impact ofreducing the Magnetic Tunnel Junction's (MTJ's) energy barrier is assessed andoptimized for the resulting stochasticity present in the learning system. Thiscan mitigate the process variation sensitivity of stochastic DBNs whichencounter a sharp drop-off when energy barriers exceed near-zero kT. Asevaluated for the MNIST dataset for energy barriers at near-zero kT to 2.0 kTin increments of 0.5 kT, it is shown that the stability factor changes by 5orders of magnitude. The self-compensating circuit developed herein provides acompact, and low complexity approach to mitigating process variation impactstowards practical implementation and fabrication.

Electrically-Tunable Stochasticity for Spin-based Neuromorphic Circuits: Self-Adjusting to Variation