Catalyst-Neuromorphic/ssc-snn-benchmark

Catalyst SSC SNN Benchmark (N3)

Spiking Neural Network for spoken command classification on SSC. Achieves 76.4% with adaptive LIF neurons.

Model Description

• Architecture (N3): 700 → 1024 (recurrent adLIF) → 512 (adLIF) → 35
• Neuron model: Adaptive Leaky Integrate-and-Fire (adLIF) with Symplectic Euler discretization
• Training: Surrogate gradient BPTT, fast-sigmoid surrogate (scale=25)
• Hardware target: Catalyst N3 neuromorphic processor

Results

Metric	Value
Float accuracy	76.4%
Parameters	2,313,763

Reproduce

git clone https://github.com/catalyst-neuromorphic/catalyst-benchmarks.git
cd catalyst-benchmarks
pip install -e .
python ssc/train.py --device cuda:0

Links

• Benchmark repo: catalyst-neuromorphic/catalyst-benchmarks
• Cloud API: catalyst-neuromorphic.com
• N3 paper: Zenodo DOI 10.5281/zenodo.18881283
• N2 paper: Zenodo DOI 10.5281/zenodo.18728256
• N1 paper: Zenodo DOI 10.5281/zenodo.18727094

Citation

@misc{catalyst-benchmarks-2026,
  author = {Shulayev Barnes, Henry},
  title = {Catalyst Neuromorphic Benchmarks},
  year = {2026},
  url = {https://github.com/catalyst-neuromorphic/catalyst-benchmarks}
}