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https://github.com/BICLab/Spike-Driven-Transformer-V2

Offical implementation of "Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips" (ICLR2024)
https://github.com/BICLab/Spike-Driven-Transformer-V2

neuromorphic-computing neuromorphic-hardware spiking-neural-networks

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Offical implementation of "Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips" (ICLR2024)

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# Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips ([ICLR2024](https://openreview.net/forum?id=1SIBN5Xyw7))



[Man Yao](https://scholar.google.com/citations?user=eE4vvp0AAAAJ), [Jiakui Hu](https://github.com/jkhu29), [Tianxiang Hu](), [Yifan Xu](https://scholar.google.com/citations?hl=zh-CN&user=pbcoTgsAAAAJ), [Zhaokun Zhou](https://scholar.google.com/citations?user=4nz-h1QAAAAJ), [Yonghong Tian](https://scholar.google.com/citations?user=fn6hJx0AAAAJ), [Bo Xu](), [Guoqi Li](https://scholar.google.com/citations?user=qCfE--MAAAAJ&)



BICLab, Institute of Automation, Chinese Academy of Sciences



---



:rocket:  :rocket:  :rocket: **News**:



- **Jan. 16, 2024**: Accepted as poster in ICLR2024.

- **Feb. 15, 2024**: Release the training and inference codes in classification tasks.



TODO:



- [x] Upload train and test scripts.

- [ ] Upload checkpoints.



## Abstract



Neuromorphic computing, which exploits Spiking Neural Networks (SNNs) on neuromorphic chips, is a promising energy-efficient alternative to traditional AI. CNN-based SNNs are the current mainstream of neuromorphic computing. By contrast, no neuromorphic chips are designed especially for Transformer-based SNNs, which have just emerged, and their performance is only on par with CNN-based SNNs, offering no distinct advantage. In this work, we propose a general Transformer-based SNN architecture, termed as "Meta-SpikeFormer", whose goals are: (1) **Lower-power**, supports the spike-driven paradigm that there is only sparse addition in the network; (2) **Versatility**, handles various vision tasks; (3) **High-performance**, shows overwhelming performance advantages over CNN-based SNNs; (4) **Meta-architecture**, provides inspiration for future next-generation Transformer-based neuromorphic chip designs. Specifically, we extend the [Spike-driven Transformer](https://github.com/BICLab/Spike-Driven-Transformer) into a meta architecture, and explore the impact of structure, spike-driven self-attention, and skip connection on its performance. On ImageNet-1K, Meta-SpikeFormer achieves **80.0% top-1 accuracy** (55M), surpassing the current state-of-the-art (SOTA) SNN baselines (66M) by 3.7%. This is the first direct training SNN backbone that can simultaneously **supports classification, detection, and segmentation**, obtaining SOTA results in SNNs. Finally, we discuss the inspiration of the meta SNN architecture for neuromorphic chip design.



![V2](./img/300_spike_driven_transformer_v2_me.png)



## Classification



### Requirements



```python3

pytorch >= 2.0.0

cupy

spikingjelly == 0.0.0.0.12

```



### Results on Imagenet-1K



The checkpoints are comming soon.



### Train & Test



The hyper-parameters are in `./conf/`.



Train:



```shell

torchrun --standalone --nproc_per_node=8 \

  main_finetune.py \

  --batch_size 128 \

  --blr 6e-4 \

  --warmup_epochs 10 \

  --epochs 200 \

  --model metaspikformer_8_512 \

  --data_path /your/data/path \

  --output_dir outputs/T1 \

  --log_dir outputs/T1 \

  --model_mode ms \

  --dist_eval

```



Finetune:



> Please download caformer_b36_in21_ft1k.pth first following [PoolFormer](https://github.com/sail-sg/poolformer).



```shell

torchrun --standalone --nproc_per_node=8 \

  main_finetune.py \

  --batch_size 24 \

  --blr 2e-5 \

  --warmup_epochs 5 \

  --epochs 50 \

  --model metaspikformer_8_512 \

  --data_path /your/data/path \

  --output_dir outputs/T4 \

  --log_dir outputs/T4 \

  --model_mode ms \

  --dist_eval \

  --finetune /your/ckpt/path \

  --time_steps 4 \

  --kd \

  --teacher_model caformer_b36_in21ft1k \

  --distillation_type hard

```



Test:



```shell

python main_finetune.py --batch_size 128 --model metaspikformer_8_512 --data_path /your/data/path --eval --resume /your/ckpt/path

```



### Data Prepare



ImageNet with the following folder structure, you can extract imagenet by this [script](https://gist.github.com/BIGBALLON/8a71d225eff18d88e469e6ea9b39cef4).



```shell

│imagenet/

├──train/

│  ├── n01440764

│  │   ├── n01440764_10026.JPEG

│  │   ├── n01440764_10027.JPEG

│  │   ├── ......

│  ├── ......

├──val/

│  ├── n01440764

│  │   ├── ILSVRC2012_val_00000293.JPEG

│  │   ├── ILSVRC2012_val_00002138.JPEG

│  │   ├── ......

│  ├── ......

```



## Contact Information



```

@inproceedings{

yao2024spikedriven,

title={Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips},

author={Man Yao and JiaKui Hu and Tianxiang Hu and Yifan Xu and Zhaokun Zhou and Yonghong Tian and Bo XU and Guoqi Li},

booktitle={The Twelfth International Conference on Learning Representations},

year={2024},

url={https://openreview.net/forum?id=1SIBN5Xyw7}

}

```



For help or issues using this git, please submit a GitHub issue.



For other communications related to this git, please contact `[email protected]` and `[email protected]`.



## Thanks



Our implementation is mainly based on the following codebases. We gratefully thank the authors for their wonderful works.



[deit](https://github.com/facebookresearch/deit)