Learning Dynamics in Memristor-Based Equilibrium Propagation

• URL: http://arxiv.org/abs/2512.12428v1
• Date: Sat, 13 Dec 2025 18:57:05 GMT
• Title: Learning Dynamics in Memristor-Based Equilibrium Propagation
• Authors: Michael Döll, Andreas Müller, Bernd Ulmann,
• Abstract summary: We investigate the effect of nonlinear, memristor-driven weight updates on the convergence behaviour of neural networks trained with equilibrium propagation (EqProp)<n>EqProp can achieve robust convergence under nonlinear weight updates, provided that memristors exhibit a sufficiently wide resistance range of at least an order of magnitude.
• Score: 0.7266320276728724
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Memristor-based in-memory computing has emerged as a promising paradigm to overcome the constraints of the von Neumann bottleneck and the memory wall by enabling fully parallelisable and energy-efficient vector-matrix multiplications. We investigate the effect of nonlinear, memristor-driven weight updates on the convergence behaviour of neural networks trained with equilibrium propagation (EqProp). Six memristor models were characterised by their voltage-current hysteresis and integrated into the EBANA framework for evaluation on two benchmark classification tasks. EqProp can achieve robust convergence under nonlinear weight updates, provided that memristors exhibit a sufficiently wide resistance range of at least an order of magnitude.

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