File size: 3,041 Bytes
0b16d18
935d06b
 
 
0b16d18
 
 
935d06b
 
 
 
0b16d18
935d06b
0b16d18
 
 
82bac7a
0b16d18
 
 
 
 
 
935d06b
0b16d18
9bf51dd
0b16d18
9bf51dd
82bac7a
 
 
 
 
 
 
 
 
0b16d18
 
82bac7a
c6e3e16
82bac7a
c6e3e16
935d06b
c6e3e16
9bf51dd
82bac7a
 
9bf51dd
82bac7a
 
c6e3e16
935d06b
c6e3e16
82bac7a
 
 
 
 
c6e3e16
935d06b
c6e3e16
935d06b
 
 
 
82bac7a
 
9bf51dd
82bac7a
 
 
 
 
 
 
 
 
 
 
 
c6e3e16
 
935d06b
b888744
935d06b
82bac7a
9bf51dd
935d06b
c6e3e16
 
 
935d06b
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
---
language: en
license: mit
library_name: pytorch
tags:
  - spiking-neural-network
  - neuromorphic
  - surrogate-gradient
  - benchmark
  - catalyst
  - shd
datasets:
  - shd
metrics:
  - accuracy
model-index:
  - name: Catalyst SHD SNN Benchmark
    results:
      - task:
          type: audio-classification
          name: Spoken Digit Classification
        dataset:
          name: Spiking Heidelberg Digits (SHD)
          type: shd
        metrics:
          - name: Float Accuracy (N3)
            type: accuracy
            value: 91.0
          - name: Float Accuracy (N2)
            type: accuracy
            value: 84.5
          - name: Float Accuracy (N1)
            type: accuracy
            value: 90.6
          - name: Quantised Accuracy (N3, int16)
            type: accuracy
            value: 90.8
---

# Catalyst SHD SNN Benchmark

Spiking Neural Network trained on the Spiking Heidelberg Digits (SHD) dataset using surrogate gradient BPTT. Achieves 91.0% on SHD with adaptive LIF neurons (90.8% quantised int16).

## Model Description

- **Architecture (N3)**: 700 β†’ 1536 (recurrent adLIF) β†’ 20
- **Architecture (N2)**: 700 β†’ 512 (recurrent adLIF) β†’ 20
- **Architecture (N1)**: 700 β†’ 1024 (recurrent LIF) β†’ 20
- **Neuron model**: Adaptive Leaky Integrate-and-Fire (adLIF) with learnable per-neuron thresholds
- **Training**: Surrogate gradient BPTT, fast-sigmoid surrogate (scale=25), cosine LR scheduling
- **Hardware target**: Catalyst N1/N2/N3 neuromorphic processors

## Results

| Generation | Architecture | Float Accuracy | Params | vs SOTA |
|------------|-------------|----------------|--------|---------|
| **N3** | 700β†’1536β†’20 (rec, adLIF) | **91.0%** | 3.47M | Matches Loihi 2 (90.9%) |
| N2 | 700β†’512β†’20 (rec, adLIF) | 84.5% | 759K | β€” |
| N1 | 700β†’1024β†’20 (rec, LIF) | 90.6% | 1.79M | Basic LIF baseline |

## Reproduce

```bash
git clone https://github.com/catalyst-neuromorphic/catalyst-benchmarks.git
cd catalyst-benchmarks
pip install -e .

# N3 (91.0%)
python shd/train.py --neuron adlif --hidden 1536 --epochs 200 --device cuda:0 --amp

# N2 (84.5%)
python shd/train.py --neuron adlif --hidden 512 --epochs 200 --device cuda:0

# N1 (90.6%)
python shd/train.py --neuron lif --hidden 1024 --epochs 200 --device cuda:0
```

## Deploy to Catalyst Hardware

```bash
python shd/deploy.py --checkpoint shd_model.pt --threshold-hw 1000
```

## Links

- **Benchmark repo**: [catalyst-neuromorphic/catalyst-benchmarks](https://github.com/catalyst-neuromorphic/catalyst-benchmarks)
- **Hardware**: [catalyst-neuromorphic.com](https://catalyst-neuromorphic.com)
- **N3 paper**: [Zenodo DOI 10.5281/zenodo.18881283](https://zenodo.org/records/18881283)
- **N2 paper**: [Zenodo DOI 10.5281/zenodo.18728256](https://zenodo.org/records/18728256)

## Citation

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