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

• URL: http://arxiv.org/abs/2404.06344v1
• Date: Tue, 9 Apr 2024 14:33:03 GMT
• Title: Synaptogen: A cross-domain generative device model for large-scale neuromorphic circuit design
• Authors: Tyler Hennen, Leon Brackmann, Tobias Ziegler, Sebastian Siegel, Stephan Menzel, Rainer Waser, Dirk J. Wouters, Daniel Bedau,
• Abstract summary: We present a fast generative modeling approach for resistive memories that reproduces the complex statistical properties of real-world devices. By training on extensive measurement data of integrated 1T1R arrays, an autoregressive process accurately accounts for the cross-correlations between the parameters. Benchmarks show that this statistically comprehensive model read/writes throughput exceeds those of even highly simplified and deterministic compact models.
• Score: 1.704443882665726
• License: http://creativecommons.org/licenses/by/4.0/
• 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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