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A fast generative model for stochastic memory cells

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Synaptogen

DOI

This is a fast generative model for stochastic memory cells. It helps determine how real-world devices would perform in large-scale circuits, for example when used as resistive weights in a neuromorphic system.

The model is trained on measurement data and closely replicates

  • cross-correlations and history dependence of switching parameters
  • cycle-to-cycle and device-to-device distributions
  • multi-level resistance states
  • resistance non-linearity

It is currently implemented in

You can check the respective subdirectories for instructions and examples.

Publications

You can learn more about the model in the following publications:

A high throughput generative vector autoregression model for stochastic synapses

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

Code authors

  • Tyler Hennen (Synaptogen.jl & Synaptogen.py)
  • Leon Brackmann (Synaptogen.va)