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Event-Based Simulation of Stochastic Memristive Devices for Neuromorphic Computing
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DOI：10.1109/TCAD.2025.3607670.png)
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Abstract
En 中文
In this article, we build a general modeling framework for memristors, suitable for the simulation of event-based systems such as hardware spiking neural networks, and more generally, neuromorphic computing systems composed of three independent components: 1) an event-based modeling approach, extending and generalizing an existing general model of memristors-the generalized metastable switch model (GMSM)-eliminating errors associated with discrete time approximation, as well as offering potential improvements in terms of suitability for neuromorphic memristive system simulations; 2) a volatility state variable to allow for the unified understanding of disparate nonlinear and volatile phenomena, including state relaxation, structural disruption, Joule heating, and nonlinear drift in different memristive devices; and 3) a readout equation that separates the latent state variable evolution from explicit variables of interest such as an instantaneous resistance. We exhibit an illustrative implementation of this framework, fit to a resistive drift dataset for titanium dioxide memristors, based on a proposed linear conductance model for resistive drift in the devices. Finally, we highlight the application of the model to neuromorphic computing by demonstrating the contribution of the volatility state variable to switching dynamics, resulting in frequency-dependent switching (for stable memristors acting as programmable synaptic weights) and the generation of action potentials (for unstable memristors, acting as spike generators).
Keywords:
Switches
Mathematical models
Resistance
Computational modeling
Memristors
Event detection
Neuromorphic engineering
Stochastic processes
Random variables
Adaptation models
Event-based models
memristors
neuromorphic computing
volatility
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