NIR - Neuromorphic Intermediate Representation

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NIR - Neuromorphic Intermediate Representation#

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NIR is a set of computational primitives, shared across different neuromorphic frameworks and technology stacks. NIR is currently supported by 7 simulators and 4 hardware platforms, allowing users to seamlessly move between any of these platforms.

NIR is useful when you want to move a model from one platform to another, for instance from a simulator to a hardware platform.

Read more about NIR in our documentation about NIR primitives

Usage#

Read more in our documentation about NIR usage and see more examples in our examples section

NIR serves as a format between neuromorphic platforms and will be installed alongside your framework of choice. Using NIR is typically a part of your favorite framework’s workflow, but follows the same pattern when you want to move from a source to a target platform:

# Define a model
my_model = ...
# Save the model (source platform)
nir.write("my_graph.nir", my_model) 
# Load the model (target platform)
imported_graph = nir.read("my_graph.nir")

See our example section for how to use NIR with your favorite framework.

Frameworks that currently support NIR#

Framework

Write to NIR

Read from NIR

Examples

Lava-DL

✓

⬚

Lava/Loihi examples

Nengo

✓

✓

Nengo examples

Norse

✓

✓

Norse examples

Rockpool (SynSense Xylo chip)

✓

✓

Rockpool/Xylo examples

Sinabs (SynSense Speck chip)

✓

✓

Sinabs/Speck examples

snnTorch

✓

✓

snnTorch examples

SpiNNaker2

⬚

✓

SpiNNaker2 examples

Spyx

✓

✓

Spyx examples

Acknowledgements#

This work was originally conceived at the Telluride Neuromorphic Workshop 2023 by the authors below (in alphabetical order):

If you use NIR in your work, please cite the following paper

article{NIR2024, 
    title={Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing}, 
    author={Pedersen, Jens E. and Abreu, Steven and Jobst, Matthias and Lenz, Gregor and Fra, Vittorio and Bauer, Felix Christian and Muir, Dylan Richard and Zhou, Peng and Vogginger, Bernhard and Heckel, Kade and Urgese, Gianvito and Shankar, Sadasivan and Stewart, Terrence C. and Sheik, Sadique and Eshraghian, Jason K.}, 
    rights={2024 The Author(s)},
    DOI={10.1038/s41467-024-52259-9}, 
    number={1},
    journal={Nature Communications}, 
    volume={15},
    year={2024}, 
    month=sep, 
    pages={8122},
}
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