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README.md
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metrics:
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- accuracy
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model-index:
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- name: Catalyst SSC SNN Benchmark
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results:
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- task:
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type: audio-classification
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name: Spiking Speech Commands (SSC)
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type: ssc
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metrics:
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- name: Float Accuracy
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type: accuracy
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value:
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- name: Quantized Accuracy (int16)
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type: accuracy
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value: 71.6
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---
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# Catalyst SSC SNN Benchmark
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Spiking Neural Network for spoken command classification on SSC.
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## Model Description
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- **Architecture**: 700 → 1024 (recurrent adLIF) → 512 (adLIF) → 35
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- **Neuron model**: Adaptive Leaky Integrate-and-Fire (adLIF) with Symplectic Euler discretization
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- **Training**: Surrogate gradient BPTT, fast-sigmoid surrogate (scale=25)
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- **Hardware target**: Catalyst
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- **Quantization**: Float32 weights -> int16, membrane decay -> 12-bit fixed-point
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## Results
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| Metric | Value |
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|--------|-------|
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| Float accuracy |
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| Quantized accuracy (int16) | 71.6% |
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| Parameters | 2,313,763 |
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| Quantization loss | 0.5% |
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## Reproduce
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python ssc/train.py --device cuda:0
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```
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## Deploy to Catalyst Hardware
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```python
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import catalyst_cloud
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client = catalyst_cloud.Client()
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result = client.simulate(
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model="catalyst-neuromorphic/ssc-snn-benchmark",
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input_data=your_spikes,
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processor="n2"
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)
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```
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## Links
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- **Benchmark repo**: [catalyst-neuromorphic/catalyst-benchmarks](https://github.com/catalyst-neuromorphic/catalyst-benchmarks)
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- **Cloud API**: [catalyst-neuromorphic.com](https://catalyst-neuromorphic.com)
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- **N2 paper**: [Zenodo DOI 10.5281/zenodo.18728256](https://zenodo.org/records/18728256)
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- **N1 paper**: [Zenodo DOI 10.5281/zenodo.18727094](https://zenodo.org/records/18727094)
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metrics:
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- accuracy
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model-index:
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- name: Catalyst SSC SNN Benchmark (N3)
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results:
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- task:
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type: audio-classification
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name: Spiking Speech Commands (SSC)
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type: ssc
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metrics:
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- name: Float Accuracy (N3)
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type: accuracy
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value: 76.4
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---
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# Catalyst SSC SNN Benchmark (N3)
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Spiking Neural Network for spoken command classification on SSC. #1 SOTA (76.4%), beating Bittar (74.2%) and Loihi 2 (69.8%).
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## Model Description
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- **Architecture (N3)**: 700 → 1024 (recurrent adLIF) → 512 (adLIF) → 35
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- **Neuron model**: Adaptive Leaky Integrate-and-Fire (adLIF) with Symplectic Euler discretization
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- **Training**: Surrogate gradient BPTT, fast-sigmoid surrogate (scale=25)
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- **Hardware target**: Catalyst N3 neuromorphic processor
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## Results
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| Metric | Value |
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|--------|-------|
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| Float accuracy | 76.4% |
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| Parameters | 2,313,763 |
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## Reproduce
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python ssc/train.py --device cuda:0
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```
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## Links
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- **Benchmark repo**: [catalyst-neuromorphic/catalyst-benchmarks](https://github.com/catalyst-neuromorphic/catalyst-benchmarks)
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- **Cloud API**: [catalyst-neuromorphic.com](https://catalyst-neuromorphic.com)
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- **N3 paper**: [Zenodo DOI 10.5281/zenodo.18881283](https://zenodo.org/records/18881283)
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- **N2 paper**: [Zenodo DOI 10.5281/zenodo.18728256](https://zenodo.org/records/18728256)
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- **N1 paper**: [Zenodo DOI 10.5281/zenodo.18727094](https://zenodo.org/records/18727094)
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