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@@ -14,7 +14,7 @@ datasets:
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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
@@ -23,34 +23,28 @@ model-index:
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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: 72.1
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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 N1/N2/N3 neuromorphic processors
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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 | 72.1% |
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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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@@ -61,23 +55,11 @@ pip install -e .
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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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-
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- ```python
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- import catalyst_cloud
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-
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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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-
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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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