Welcome to SANA-FE’s Documentation

Architecting new neuromorphic chips involves several design decisions that can affect overall system power consumption and performance. Architecture models can be used to estimate the impact of different design decisions and thus inform decision-making and exploration. SANA-FE (Simulating Advanced Neuromorphic Architectures for Fast Exploration) is an open-source tool developed in a collaboration between UT Austin and Sandia National Laboratories (SNL) to rapidly and accurately model and simulate the energy and performance of different neuromorphic hardware platforms.

SANA-FE requires a description of the architecture you want to simulate, a specification of the spiking neural network (SNN) application you wish to max and execute. Optionally, you can also provide shared library hardware plugins and simulation configuration. SANA-FE will simulate the given architecture and give performane and energy estimates. Optionally, SANA-FE will generate detailed hardware traces.

The SANA-FE kernel is primarily written in C++, but uses PyBind11 to generate Python interfaces. SANA-FE can alternatively be used as a standalone C++ simulator. For more information on using SANA-FE without Python, visit the project repository homepage at: https://github.com/SLAM-Lab/SANA-FE The SANA-FE Python interface supports creating mapped SNNs, launching simulations, and getting prediction data out for analysis.

Quick-start

To install SANA-FE from PyPI, run the following

pip install sanafe

If you would rather install the latest SANA-FE from source, run

git clone https://github.com/SLAM-Lab/SANA-FE sana-fe
cd sana-fe
pip install .

You can test your SANA-FE installation by running the following Python code:

import sanafe
arch, snn = sanafe.load_example()
chip = sanafe.SpikingChip(arch)
chip.load(snn)
chip.sim(100)

If this ran successfully, you should see the following print on the console

Executed steps: [100/100]

For more examples of how to use SANA-FE, we have provided a set of Jupyter notebooks at: https://github.com/SLAM-Lab/SANA-FE/blob/main/tutorial

These tutorials can be run locally or using Google Colab.

Contents:

• Core API Reference (sanafe)
  • Quickstart
  • Core Classes
  • Utility Functions
• SNN Layers (sanafe.layers)
  • Quick Start
  • Layer Base Class
  • Input Layers
  • Convolutional Layers
  • Dense Layers
  • Application Examples
  • Error Handling
  • See Also
• Models (sanafe.models)
  • Framework attributes
  • accumulator
  • accumulator_with_delay
  • current_based
  • input
  • leaky_integrate_and_fire
  • taps
  • truenorth
• Trace Data Conversion (sanafe.data)
  • Quickstart
• Visualization (sanafe.viz)
  • Quickstart
  • Spike Plots
  • Membrane Potential Plots
  • Performance Plots
  • Styling

Indices and tables

• Index

• Module Index

• Search Page

Citation

We hope that you find this project useful. If you use SANA-FE in your work, please cite our paper:

@article{boyle2025sanafe,
  title={SANA-FE: Simulating Advanced Neuromorphic Architectures for Fast Exploration},
  author={James A. Boyle and Mark Plagge and Suma George Cardwell and Frances S. Chance and Andreas Gerstlauer},
  journal={IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD)},
  volume={44},
  number={8},
  pages={3165--3178},
  year={2025},
  doi={10.1109/TCAD.2025.3537971}
}

References

James A. Boyle, Jason Ho, Mark Plagge, Suma George Cardwell, Frances S. Chance, and Andreas Gerstlauer, “Exploring Dendrites in Large-Scale Neuromorphic Architectures,” in International Conference on Neuromorphic Systems (ICONS), Seattle, WA, USA, 2025, doi:10.1109/ICONS69015.2025.00018.

James A. Boyle, Mark Plagge, Suma George Cardwell, Frances S. Chance, and Andreas Gerstlauer, “SANA-FE: Simulating Advanced Neuromorphic Architectures for Fast Exploration,”in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 44, no. 8, pp. 3165–3178, 2025, doi:10.1109/TCAD.2025.3537971.

James A. Boyle, Mark Plagge, Suma George Cardwell, Frances S. Chance, and Andreas Gerstlauer, “Tutorial: Large-Scale Spiking Neuromorphic Architecture Exploration using SANA-FE,” in International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), Raleigh, NC, USA, 2024, doi:10.1109/CODES-ISSS60120.2024.00007.

James A. Boyle, Mark Plagge, Suma George Cardwell, Frances S. Chance, and Andreas Gerstlauer, “Performance and Energy Simulation of Spiking Neuromorphic Architectures for Fast Exploration,” in International Conference on Neuromorphic Systems (ICONS), Santa Fe, NM, USA, 2023, doi:10.1145/3589737.3605970.

Acknowledgement

Copyright (c) 2026 - The University of Texas at Austin This work was produced under contract #2317831 to National Technology and Engineering Solutions of Sandia, LLC which is under contract No. DE-NA0003525 with the U.S. Department of Energy.