Synaptogen: A cross-domain generative device model for large-scale neuromorphic circuit design
<|reference_start|>Synaptogen: A cross-domain generative device model for large-scale neuromorphic circuit design: 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.<|reference_end|>
@article{hennen2024synaptogen:,
    title={Synaptogen: A cross-domain generative device model for large-scale
  neuromorphic circuit design},
    author={Tyler Hennen, Leon Brackmann, Tobias Ziegler, Sebastian Siegel,
  Stephan Menzel, Rainer Waser, Dirk J. Wouters and Daniel Bedau},
    journal={arXiv preprint arXiv:2404.06344},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06344},
    primaryClass={cs.NE cond-mat.mtrl-sci eess.SP}
}