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Status: both submitted on 2026-09-15, after the 0.3.5 release put the corrected README and metadata on PyPI.
- NIR framework support table: neuromorphs/NIR#196
- Open Neuromorphic software guide: open-neuromorphic.github.io#504
The NIR pull request deviates from the draft below in one place, deliberately.
The draft assumed a plain ✓ for Read from NIR. spikeforge loads a NIR graph
and executes it through the independent interpreter; it does not
reconstruct a native module the way the other listed frameworks do
(nir_bridge/stage_builders.py and stage_mapping.py are export-direction
only, and nothing builds a TopologySpec from a graph). The pull request
states that difference and invites the maintainers to mark the cell ⬚ if they
read the column as "convert to native" -- overclaiming a cell in the table
this project's positioning rests on would cost more than the row is worth.
Searching for the name already works: "spikeforge spiking neural network" returns the GitHub repository and all seven PyPI projects at the top. What does not work is categorical discovery — someone browsing the NIR ecosystem, or searching "SNN framework comparison", never encounters the project. Two directories account for almost all of that traffic, both are free, and both explicitly invite submissions.
This is deliberately sequenced after the PyPI and documentation fixes. Reviewers of either submission will click straight through to https://pypi.org/project/spikeforge/ and https://docs.spikeforge.net/, so those pages had to be right first.
Where: https://github.com/neuromorphs/NIR — the README's framework support table.
Verified state (2026-09-14): ten frameworks listed, with columns Framework, Write to NIR, Read from NIR, Examples:
| Listed framework | Write | Read |
|---|---|---|
| hxtorch (BrainScaleS-2) | ✓ | ✓ |
| jaxsnn (BrainScaleS-2) | — | ✓ |
| Lava-DL | — | ✓ |
| Nengo | ✓ | ✓ |
| Norse | ✓ | ✓ |
| Rockpool (SynSense Xylo) | ✓ | ✓ |
| Sinabs (SynSense Speck) | ✓ | ✓ |
| snnTorch | ✓ | ✓ |
| SpiNNaker2 | — | ✓ |
| Spyx | ✓ | ✓ |
spikeforge is absent, despite supporting both directions. The README has no documented contribution process for the table, so the route is a pull request adding the row and a linked example.
What makes the row worth accepting rather than just longer: none of the ten re-executes an exported graph through an independent interpreter and reports numerical drift against the original model. That is a NIR-specific capability, which is exactly the argument to make — not "please list us."
Draft row:
| [spikeforge](https://github.com/Capsize-Games/spikeforge) | ✓ | ✓ | [spikeforge examples](https://github.com/Capsize-Games/spikeforge/blob/main/examples/04_nir_export_validate.py) |Draft PR description:
Adds spikeforge to the framework support table.
spikeforge (BSD-3-Clause,
pip install spikeforge) reads and writes NIR throughspikeforge.nir_bridge. Beyond export/import it ships an independent NIR interpreter that re-executes the exported graph and reports per-tensor numerical drift against the source model, so an export can be validated rather than assumed:pip install "spikeforge[nir]" spikeforge-verify validate --topology conv_net # non-zero exit outside toleranceRunnable example:
examples/04_nir_export_validate.py. Docs: https://docs.spikeforge.net/interpreter-spine.
Before filing, check: that examples/04_nir_export_validate.py still runs
end to end on a clean install, and that the drift claim in
Interpreter spine still matches the shipped
tolerance.
Where: https://open-neuromorphic.org/neuromorphic-computing/software/ — the de-facto directory for this field.
Verified state (2026-09-14): 27 SNN frameworks, 8 data tools, 1 simulator. Neither "spikeforge" nor "Capsize" appears. The page states:
"Help us keep the software guide comprehensive and up-to-date. Suggest new frameworks, data tools, or corrections by opening an issue on our GitHub repository."
Route: an issue at https://github.com/open-neuromorphic/open-neuromorphic.github.io/issues/new/choose.
Draft issue body:
Framework: spikeforge Repository: https://github.com/Capsize-Games/spikeforge Docs: https://docs.spikeforge.net/ Install:
pip install spikeforgeLicense: BSD-3-Clause Language / backend: Python, built on snnTorch and PyTorchWhat it is: an SNN toolkit that sits on top of snnTorch rather than replacing it. Rate/latency/delta/random spike encoding, LIF training across eight image datasets and four neuromorphic event datasets, NIR export with an independent interpreter that re-executes the exported graph and reports numerical drift, a per-target deployment capability matrix, an event-driven energy estimator (labelled as an estimate, not hardware-measured), and a live browser training/introspection dashboard.
Maturity: pre-1.0, published on PyPI, CI green, reference accuracy numbers published at https://docs.spikeforge.net/benchmarks.
Before filing, check: the maturity sentence is still true, and that the benchmarks page is live at that URL.
Neither submission is a place to claim more than the project delivers. The
capability matrix's spec-only targets (spinnaker2, speck, xylo) are
registered but have no installable SDK integration; the energy figure is an
estimate; the model hub carries a handful of this project's own reference
checkpoints, not a zoo. Every one of those is stated plainly on the project's
own surfaces, and the submissions must not soften any of it — a listing
earned by overclaiming is worse than no listing.
- Home
- Architecture
- Backend Execution
- Benchmarks
- Dashboard
- Development
- Event Datasets
- Event Runtime And Energy
- Features
- Implications And Boundaries
- Interop Foldins
- Interpreter Spine
- Introspection
- Model Deployment
- Model Hub
- Notes
- Operational Maturity
- Production Workflows
- Project Layout
- Quickstart
- Requirements
- Sequence Primitives
- Streaming Timeseries
- Targets And Interop
- Usage
- Arch 0001 Adr Repo Topology
- Arch 0001 Core Boundary
- Arch 0001 Decision Metrics
- Arch 0001 Migration Plan
- Arch 0001 Packaging Versioning
- Arch 0001 Protocol Contract
- Arch 0001 Risk Register
- Arch 0001 Target Topology
- Backend Execution Plan
- Ecosystem Listings
- Ecosystem Roadmap
- Event Runtime Plan
- Hub Expansion Plan
- Plans
- Interop Foldins Plan
- Interpreter Spine Plan
- Memory System Research
- Model Hub Plan
- Operations Plan
- Production Toolkit Plan
- Production Use Cases
- Professional Roadmap
- Repo Topology Plan
- Sequence Primitives Plan
- Use Case Audio Keyword Spotting
- Use Case Biosignal Medical Monitoring
- Use Case Computational Neuroscience
- Use Case Edge Power Budgets
- Use Case Event Camera Vision
- Use Case Intrusion Anomaly Detection
- Use Case Low Latency Sensor Stream
- Use Case Rl Control Robotics
- Use Case Spiking Transformers
- Use Case Streaming Timeseries