Event-Based Simulation of Stochastic Memristive Devices for Neuromorphic Computing

2024-06-14

arXiv.org

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.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• Modeling memristors for event-based neuromorphic computing simulations
• Unifying volatile phenomena in memristive devices with a volatility variable
• Separating latent state evolution from explicit readout variables in memristor models

Innovation

• Event-based modeling eliminates discrete time approximation errors
• Volatility state variable unifies nonlinear and volatile phenomena
• Separate readout equation isolates latent state from explicit variables