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

arXiv: https://arxiv.org/abs/2404.06344 · HTML: https://mlsanity.com/paper/2404.06344/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2024.

## Built on by (most cited first, 2 of 2)

- [Running Conventional Automatic Speech Recognition on Memristor Hardware: A Simulated Approach](https://mlsanity.com/paper/2505.24721/) (2025)
- [Positional Encoding in the Context of Memristor-Based Analog Computation for Automatic Speech Recognition](https://mlsanity.com/paper/2606.13379/) (2026)

## Abstract (arXiv)

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.