Integration of Physics-Derived Memristor Models with Machine Learning Frameworks
<|reference_start|>Integration of Physics-Derived Memristor Models with Machine Learning Frameworks: 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.<|reference_end|>
@article{yu2024integration,
    title={Integration of Physics-Derived Memristor Models with Machine Learning
  Frameworks},
    author={Zhenming Yu, Stephan Menzel, John Paul Strachan, Emre Neftci},
    journal={56th Asilomar Conference on Signals, Systems, and Computers,
  Pacific Grove, CA, USA, 2022, pp. 1142-1146},
    year={2024},
    doi={10.1109/IEEECONF56349.2022.10052010},
    archivePrefix={arXiv},
    eprint={2403.06746},
    primaryClass={cs.ET}
}