Synaptogen: A cross-domain generative device model for large-scale neuromorphic circuit design
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866917814582902784 |
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| author | Hennen, Tyler Brackmann, Leon Ziegler, Tobias Siegel, Sebastian Menzel, Stephan Waser, Rainer Wouters, Dirk J. Bedau, Daniel |
| author_facet | Hennen, Tyler Brackmann, Leon Ziegler, Tobias Siegel, Sebastian Menzel, Stephan Waser, Rainer Wouters, Dirk J. Bedau, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_06344 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Synaptogen: A cross-domain generative device model for large-scale neuromorphic circuit design Hennen, Tyler Brackmann, Leon Ziegler, Tobias Siegel, Sebastian Menzel, Stephan Waser, Rainer Wouters, Dirk J. Bedau, Daniel Neural and Evolutionary Computing Materials Science Signal Processing 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. |
| title | Synaptogen: A cross-domain generative device model for large-scale neuromorphic circuit design |
| topic | Neural and Evolutionary Computing Materials Science Signal Processing |
| url | https://arxiv.org/abs/2404.06344 |