Stochastic Memristor Modeling Framework Based on Physics-Informed Neural Networks

In this paper, we present a framework of modeling memristor noise for circuit simulators using physics-informed neural networks (PINNs). The variability of the memristor that is directly related to the neuromorphic system can be handled with this approach. The memristor noise model is transformed in...

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Veröffentlicht in:Applied sciences 2024-10, Vol.14 (20), p.9484
Hauptverfasser: Kim, Kyeongmin, Lee, Jonghwan
Format: Artikel
Sprache:eng
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Zusammenfassung:In this paper, we present a framework of modeling memristor noise for circuit simulators using physics-informed neural networks (PINNs). The variability of the memristor that is directly related to the neuromorphic system can be handled with this approach. The memristor noise model is transformed into a Fokker–Planck equation (FPE) from a probabilistic perspective. The translated equations are physically interpreted through the PINN. The weights and biases extracted from the PINN are implemented in Verilog-A through simple operations. The characteristics of the stochastic system under the noise are obtained by integrating the probability density function. This approach allows for the unification of different memristor models and the analysis of the effects of noise.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14209484