🤖 AI Summary
Existing memristor models for event-driven neuromorphic systems—such as spiking neural networks—suffer from discretization-induced timing errors and poor generalizability due to reliance on fixed-time-step numerical simulation.
Method: This paper introduces the first general-purpose, event-driven memristor modeling framework. It unifies volatile state variables to capture diverse failure mechanisms; reformulates the generalized metastable switch (MSS) model in event-driven form; and integrates physics-informed filament growth dynamics with a linear conductance drift model, calibrated using experimentally measured drift data from titanium oxide devices.
Contribution/Results: The framework eliminates temporal discretization error, enabling high-fidelity, low-overhead stochastic behavior simulation. It supports large-scale, brain-inspired hardware–software co-simulation, significantly improving model portability across platforms and enhancing physical interpretability through mechanistic fidelity.
📝 Abstract
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.