  Magnetic Random-Access Memory (MRAM) based p-bit neuromorphic computing
devices are garnering increasing interest as a means to compactly and
efficiently realize machine learning operations in Restricted Boltzmann
Machines (RBMs). When embedded within an RBM resistive crossbar array, the
p-bit based neuron realizes a tunable sigmoidal activation function. Since the
stochasticity of activation is dependent on the energy barrier of the MRAM
device, it is essential to assess the impact of process variation on the
voltage-dependent behavior of the sigmoid function. Other influential
performance factors arise from varying energy barriers on power consumption
requiring a simulation environment to facilitate the multi-objective
optimization of device and network parameters. Herein, transportable Python
scripts are developed to analyze the output variation under changes in device
dimensions on the accuracy of machine learning applications. Evaluation with
RBM circuits using the MNIST dataset reveal impacts and limits for processing
variation of device fabrication in terms of the resulting energy vs. accuracy
tradeoffs, and the resulting simulation framework is available via a Creative
Commons license.
