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arXiv · 2404.06344

Synaptogen: A cross-domain generative device model for large-scale neuromorphic circuit design

Tyler Hennen·Leon Brackmann·Tobias Ziegler·Sebastian Siegel·Stephan Menzel·Rainer Waser·Dirk J. Wouters·Daniel Bedau
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Abstract

We present a fast generative modeling approach for resistive memories that reproduces the complex statistical properties of real-world devices. To enable efficient modeling of analog circuits, the model is implemented in Verilog-A. By training on extensive measurement data of integrated 1T1R arrays (6,000 cycles of 512 devices), an autoregressive stochastic process accurately accounts for the cross-correlations between the switching parameters, while non-linear transformations ensure agreement with both cycle-to-cycle (C2C) and device-to-device (D2D) variability. Benchmarks show that this statistically comprehensive model achieves read/write throughputs exceeding those of even highly simplified and deterministic compact models.

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Tyler Hennen, Leon Brackmann, Tobias Ziegler, Sebastian Siegel, Stephan Menzel, Rainer Waser, Dirk J. Wouters, Daniel Bedau. 2024-04-09. Synaptogen: A cross-domain generative device model for large-scale neuromorphic circuit design. https://arxiv.org/abs/2404.06344

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