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Spikingformer

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Spikingformer: A Key Foundation Model for Spiking Neural Networks (AAAI 2026)

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# Spikingformer: A Key Foundation Model for Spiking Neural Networks, [AAAI 2026](https://arxiv.org/abs/2304.11954)

# Spikingformer: Spike-driven Residual Learning for Transformer-based Spiking Neural Network, [Arxiv 2023](https://arxiv.org/abs/2304.11954)
Spikingformer is a pure event-driven transformer-based spiking neural network (**75.85% top-1** accuracy on ImageNet-1K, **+ 1.04%** and **significantly reduces energy consumption by 57.34%** compared with Spikformer). To our best knowledge, this is the first time that **a pure event-driven transformer-based SNN** has been developed in 2023/04.

## News
[2025.11.8] Accepted by AAAI 2026.

[2024.2.23] Update energy_consumption_calculation of Spikingformer or Spikformer on ImageNet.

[2023.9.11] Update origin_logs and cifar10 trained model.

[2023.8.18] Update trained models.

## Reference
If you find this repo useful, please consider citing:
```
@article{zhou2023spikingformer,
  title={Spikingformer: Spike-driven Residual Learning for Transformer-based Spiking Neural Network},
  author={Zhou, Chenlin and Yu, Liutao and Zhou, Zhaokun and Zhang, Han and Ma, Zhengyu and Zhou, Huihui and Tian, Yonghong},
  journal={arXiv preprint arXiv:2304.11954},
  year={2023},
  url={https://arxiv.org/abs/2304.11954}
}

@article{zhou2024direct,
  title={Direct training high-performance deep spiking neural networks: a review of theories and methods},
  author={Zhou, Chenlin and Zhang, Han and Yu, Liutao and Ye, Yumin and Zhou, Zhaokun and Huang, Liwei and Ma, Zhengyu and Fan, Xiaopeng and Zhou, Huihui and Tian, Yonghong},
  journal={Frontiers in Neuroscience},
  volume={18},
  pages={1383844},
  year={2024},
  publisher={Frontiers Media SA}
}
```

## Main results on ImageNet-1K

| Model               | Resolution| T |  Param.     | FLOPs   |  Power |Top-1 Acc| Download |
| :---:               | :---:     | :---:  | :---:       |  :---:  |  :---:    |:---: |:---: |
| Spikingformer-8-384 | 224x224   | 4 |  16.81M     | 3.88G   | 4.69 mJ   |72.45  |   -    |
| Spikingformer-8-512 | 224x224   | 4 |  29.68M     | 6.52G  | 7.46 mJ   |74.79  |     -  |
| Spikingformer-8-768 | 224x224   | 4  |  66.34M     | 12.54G  | 13.68 mJ  |75.85  |   [here](https://pan.baidu.com/s/1LsECpFOxh30O3vHWow8OGQ) |

All download passwords: abcd

## Main results on CIFAR10/CIFAR100

| Model                | T      |  Param.     | CIFAR10 Top-1 Acc| Download  |CIFAR100 Top-1 Acc|
| :---:                | :---:  | :---:       |  :---:  |:---:   |:---: |
| Spikingformer-4-256  | 4      |  4.15M     | 94.77   |   -   |77.43  |
| Spikingformer-2-384  | 4      |  5.76M     | 95.22   |   -   |78.34  |
| Spikingformer-4-384  | 4      |  9.32M     | 95.61    |   -  |79.09  |
| Spikingformer-4-384-400E  | 4      |  9.32M     | 95.81    | [here](https://pan.baidu.com/s/1mjpD2gtz5ZX0M8N3jobjzA ) |79.21  |

All download passwords: abcd

## Main results on CIFAR10-DVS/DVS128

| Model               | T      |  Param.     |  CIFAR10 DVS Top-1 Acc  | DVS 128 Top-1 Acc|
| :---:               | :---:  | :---:       | :---:                   |:---:            |
| Spikingformer-2-256 | 10     |  2.57M      | 79.9                    | 96.2            |
| Spikingformer-2-256 | 16     |  2.57M      | 81.3                    | 98.3            |

## Requirements
timm==0.6.12; cupy==11.4.0; torch==1.12.1; spikingjelly==0.0.0.0.12; pyyaml;

data prepare: ImageNet with the following folder structure, you can extract imagenet by this [script](https://gist.github.com/BIGBALLON/8a71d225eff18d88e469e6ea9b39cef4).
```
│imagenet/
├──train/
│  ├── n01440764
│  │   ├── n01440764_10026.JPEG
│  │   ├── n01440764_10027.JPEG
│  │   ├── ......
│  ├── ......
├──val/
│  ├── n01440764
│  │   ├── ILSVRC2012_val_00000293.JPEG
│  │   ├── ILSVRC2012_val_00002138.JPEG
│  │   ├── ......
│  ├── ......
```

## Train
### Training  on ImageNet
Setting hyper-parameters in imagenet.yml

```
cd imagenet
python -m torch.distributed.launch --nproc_per_node=8 train.py
```

### Testing ImageNet Val data
Download the trained model first [here](https://pan.baidu.com/s/1LsECpFOxh30O3vHWow8OGQ), passwords: abcd
```
cd imagenet
python test.py
```

### Training  on CIFAR10
Setting hyper-parameters in cifar10.yml
```
cd cifar10
python train.py
```

### Training  on CIFAR100
Setting hyper-parameters in cifar100.yml
```
cd cifar10
python train.py
```

### Training  on DVS128 Gesture
```
cd dvs128-gesture
python train.py
```

### Training  on CIFAR10-DVS
```
cd cifar10-dvs
python train.py
```

### Energy Consumption Calculation on ImageNet
Download the trained model first [here](https://pan.baidu.com/s/1LsECpFOxh30O3vHWow8OGQ), passwords: abcd
```
cd imagenet
python energy_consumption_calculation_on_imagenet.py
```

## A Handwriting Error Correction in Manuscript
In neuromorphic datasets, the preprocessing (transforming events into frames) of neuromorphic datasets is according to SEW or SpikingJelly. The event stream comprises four dimensions: the event’s coordinate (x, y), time (t), and polarity (p). We split the event’s number N into T (the simulating time-step) slices with nearly the same number of events in each slice and integrate events into frames. It is a pity that Equation 20 in the manuscript is a formula mistake, we corrected it as follows:
$$E_{Spikingformer}^{neuro}=E_{A C} \times\left(\sum_{i=2}^N S O P_{{Conv} }^i+\sum_{j=1}^M S O P_{{SSA}}^j\right)+E_{M A C} \times\left(FLOP_{{Conv}}^1\right)$$

## Acknowledgement & Contact Information
Related project: [spikformer](https://github.com/ZK-Zhou/spikformer), [pytorch-image-models](https://github.com/huggingface/pytorch-image-models), [CML](https://github.com/zhouchenlin2096/Spikingformer-CML), [spikingjelly](https://github.com/fangwei123456/spikingjelly).

The code of Spikingformer<sup>†</sup>: [link](https://github.com/zhouchenlin2096/Spikingformer-CML)

Spikingformer-8-768 on Google Drive: [link](https://drive.google.com/drive/folders/1DE4qm-9vKNwDfJAdYYrDdPFc7_cGo2nK?usp=drive_link)

Spikingformer-4-384-400E on Google Drive: [link](https://drive.google.com/drive/folders/1LES30-AsGM02xkWftpUeh_WudhwDZzzW?usp=drive_link)

Spikingformer

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