Event-Based Simulation of Stochastic Memristive Devices for Neuromorphic Computing
<|reference_start|>Event-Based Simulation of Stochastic Memristive Devices for Neuromorphic Computing: In this paper, we build a general model of memristors suitable for the simulation of event-based systems, such as hardware spiking neural networks, and more generally, neuromorphic computing systems. We extend an existing general model of memristors - the Generalised Metastable Switch Model - to an event-driven setting, eliminating errors associated discrete time approximation, as well as offering potential improvements in terms of computational efficiency for simulation. We introduce the notion of a volatility state variable, to allow for the modelling of memory-dependent and dynamic switching behaviour, succinctly capturing and unifying a variety of volatile phenomena present in memristive devices, including state relaxation, structural disruption, Joule heating, and drift acceleration phenomena. We supply a drift dataset for titanium dioxide memristors and introduce a linear conductance model to simulate the drift characteristics, motivated by a proposed physical model of filament growth. We then demonstrate an approach for fitting the parameters of the event-based model to the drift model.<|reference_end|>
@article{el-geresy2024event-based,
    title={Event-Based Simulation of Stochastic Memristive Devices for Neuromorphic
  Computing},
    author={Waleed El-Geresy, Christos Papavassiliou, Deniz G"und"uz},
    journal={arXiv preprint arXiv:2407.04718},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04718},
    primaryClass={cs.ET cs.NE physics.app-ph}
}