  Simulation frameworks such MemTorch, DNN+NeuroSim, and aihwkit are commonly
used to facilitate the end-to-end co-design of memristive machine learning (ML)
accelerators. These simulators can take device nonidealities into account and
are integrated with modern ML frameworks. However, memristors in these
simulators are modeled with either lookup tables or simple analytic models with
basic nonlinearities. These simple models are unable to capture certain
performance-critical aspects of device nonidealities. For example, they ignore
the physical cause of switching, which induces errors in switching timings and
thus incorrect estimations of conductance states. This work aims at bringing
physical dynamics into consideration to model nonidealities while being
compatible with GPU accelerators. We focus on Valence Change Memory (VCM)
cells, where the switching nonlinearity and SET/RESET asymmetry relate tightly
with the thermal resistance, ion mobility, Schottky barrier height, parasitic
resistance, and other effects. The resulting dynamics require solving an ODE
that captures changes in oxygen vacancies. We modified a physics-derived
SPICE-level VCM model, integrated it with the aihwkit simulator and tested the
performance with the MNIST dataset. Results show that noise that disrupts the
SET/RESET matching affects network performance the most. This work serves as a
tool for evaluating how physical dynamics in memristive devices affect neural
network accuracy and can be used to guide the development of future integrated
devices.
