Quantized Spike-driven Transformer (ICLR25)
Xuerui Qiu, Malu Zhang, Jieyuan Zhang, Wenjie Wei, Honglin Cao, Junsheng Guo, Rui-Jie Zhu,Yimeng Shan,Yang Yang, Haizhou Li
University of Electronic Science and Technology of China
Institute of Automation, Chinese Academy of Sciences
Quantization Model ImageNet From Scratch: See Train_Base.md.
Object Detection: See Detection.md.
Semantic Segmentation: See Segementation.md
🚀 🚀 🚀 News:
- Feb. 24, 2025: Checkpoint is available at Huggingface.
- Jan. 24, 2025: Release the code for training and testing.
Spiking neural networks (SNNs) are emerging as a promising energy-efficient alternative to traditional artificial neural networks (ANNs) due to their spike-driven paradigm. However, recent research in the SNN domain has mainly focused on enhancing accuracy by designing large-scale Transformer structures, which typically rely on substantial computational resources, limiting their deployment on resource-constrained devices. To overcome this challenge, we propose a quantized spike-driven Transformer baseline (QSD-Transformer), which achieves reduced resource demands by utilizing a low bit-width parameter. Regrettably, the QSD-Transformer often suffers from severe performance degradation. In this paper, we first conduct empirical analysis and find that the bimodal distribution of quantized spike-driven self-attention (Q-SDSA) leads to spike information distortion (SID) during quantization, causing significant performance degradation. To mitigate this issue, we take inspiration from mutual information entropy and propose a bi-level optimization strategy to rectify the information distribution in Q-SDSA. Specifically, at the lower level, we introduce an information-enhanced LIF to rectify the information distribution in Q-SDSA. At the upper level, we propose a fine-grained distillation scheme for the QSD-Transformer to align the distribution in Q-SDSA with that in the counterpart ANN. By integrating the bi-level optimization strategy, the QSD-Transformer can attain enhanced energy efficiency without sacrificing its high-performance advantage. We validate the QSD-Transformer on various visual tasks, and experimental results indicate that our method achieves state-of-the-art results in the SNN domain. For instance, when compared to the prior SNN benchmark on ImageNet, the QSD-Transformer achieves 80.3% top-1 accuracy, accompanied by significant reductions of 6.0x and 8.1x in power consumption and model size, respectively.
In this paper, we first introduce the lightweight spike-driven transformer, namely the QSDTransformer, which quantifies the weights from 32-bit to low-bit. By employing both low-bit weights and 1-bit spike activities, QSD-Transformer has demonstrated significant energy efficiency. Despite exhibiting efficiency benefits, the QSD-Transformer suffers from performance degradation. We reveal that this is attributed to the SID problem and propose a bi-level optimization strategy to solve this challenge. At the lower level, we propose the IE-LIF neuron, which generates multi-bit spikes in training while maintaining spike-driven behavior during inference. At the upper level, we introduce the FGD scheme, which optimizes attention distribution between the Q-SDSA and its ANN counterpart. Extensive experiments show that our method achieves state-of-the-art results in both performance and efficiency on various vision tasks, paving the way for the practical deployment of spike-based Transformers in resource-limited platforms.
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@inproceedings{qiu2025quantized,
title={Quantized Spike-driven Transformer},
author={Xuerui Qiu and Jieyuan Zhang and Wenjie Wei and Honglin Cao and Junsheng Guo and Rui-Jie Zhu and Yimeng Shan and Yang Yang and Malu Zhang and Haizhou Li},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=5J9B7Sb8rO}
}
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The object detection and semantic segmentation parts are based on MMDetection and MMSegmentation respectively. Thanks for their wonderful work.