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

• Julia for machine learning and general purpose programming (Synaptogen.jl)
• Python (NumPy) (Synaptogen.py)
• Verilog-A for circuit-level simulations (Synaptogen.va)

You can check the respective subdirectories for instructions and examples.

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

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