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Emerging technologies based on resistive switching (ReRAM) devices promise to improve the speed and energy efficiency of next generation machine learning accelerators, but further research is required for achieving commercial maturity. System-level prototyping with emerging devices is costly, and algorithmic investigations require hardware neural network modeling which often deviates from experimental reality. In this work, the concept of modeling bias is proposed as a way to quantify this deviation and support reliable evaluation of device populations in the context of neural network algorithms. While applicable to other device modeling techniques, modeling bias is investigated here using jump tables - a promising physics-less technique to model emerging memory devices for hardware networks. Questions about the fidelity of these tables in relation to stochastic device behavior are answered. Two methods …