Neural Dynamics Self-Attention for Spiking Transformers
arXiv:2603.19290v1 Announce Type: cross Abstract: Integrating Spiking Neural Networks (SNNs) with Transformer architectures offers a promising pathway to balance energy efficiency and performance, particularly for edge vision applications. However, existing Spiking Transformers face two critical challenges: (i) a substantial performance gap compared to their Artificial Neural Networks (ANNs) counterparts and (ii) high memory overhead during inference. Through theoretical analysis, we attribute both limitations to the Spiking Self-Attention (SSA) mechanism: the lack of locality bias and the need to store large attention matrices. Inspired by the localized receptive fields (LRF) and membrane-potential dynamics of biological visual neurons, we propose LRF-Dyn, which uses spiking neurons with localized receptive fields to compute attention while reducing memory requirements. Specifically, we introduce a LRF method into SSA to assign higher weights to neighboring regions, strengthening loc
arXiv:2603.19290v1 Announce Type: cross Abstract: Integrating Spiking Neural Networks (SNNs) with Transformer architectures offers a promising pathway to balance energy efficiency and performance, particularly for edge vision applications. However, existing Spiking Transformers face two critical challenges: (i) a substantial performance gap compared to their Artificial Neural Networks (ANNs) counterparts and (ii) high memory overhead during inference. Through theoretical analysis, we attribute both limitations to the Spiking Self-Attention (SSA) mechanism: the lack of locality bias and the need to store large attention matrices. Inspired by the localized receptive fields (LRF) and membrane-potential dynamics of biological visual neurons, we propose LRF-Dyn, which uses spiking neurons with localized receptive fields to compute attention while reducing memory requirements. Specifically, we introduce a LRF method into SSA to assign higher weights to neighboring regions, strengthening local modeling and improving performance. Building on this, we approximate the resulting attention computation via charge-fire-reset dynamics, eliminating explicit attention-matrix storage and reducing inference-time memory. Extensive experiments on visual tasks confirm that our method reduces memory overhead while delivering significant performance improvements. These results establish it as a key unit for achieving energy-efficient Spiking Transformers.
Executive Summary
This article proposes a novel approach to Spiking Transformers, called LRF-Dyn, which addresses two critical challenges faced by existing Spiking Transformers: performance gap with ANNs and high memory overhead. By incorporating localized receptive fields and membrane-potential dynamics, LRF-Dyn enhances local modeling and reduces memory requirements. Extensive experiments demonstrate significant performance improvements and reduced memory overhead, making LRF-Dyn a promising unit for energy-efficient Spiking Transformers. The method's ability to approximate attention computation via charge-fire-reset dynamics offers a potential solution to the scalability issue of Spiking Transformers.
Key Points
- ▸ LRF-Dyn addresses the performance gap and high memory overhead challenges in Spiking Transformers.
- ▸ Localized receptive fields and membrane-potential dynamics enhance local modeling and reduce memory requirements.
- ▸ Extensive experiments demonstrate significant performance improvements and reduced memory overhead.
Merits
Strength in Local Modeling
LRF-Dyn's use of localized receptive fields strengthens local modeling, leading to improved performance and reduced memory requirements.
Efficient Inference
The charge-fire-reset dynamics approximation reduces inference-time memory overhead, making LRF-Dyn a scalable solution for Spiking Transformers.
Demerits
Complexity
The incorporation of localized receptive fields and membrane-potential dynamics may increase the complexity of the LRF-Dyn architecture, requiring further optimization and fine-tuning.
Limited Generalizability
The performance improvements and reduced memory overhead demonstrated in the article are specific to visual tasks and may not generalize to other application domains.
Expert Commentary
The article presents a significant advancement in the field of Spiking Transformers, addressing two critical challenges that have limited their adoption. LRF-Dyn's innovative approach to local modeling and attention computation offers a promising solution for energy-efficient AI, particularly in edge vision applications. However, the complexity and limited generalizability of the architecture require further investigation and optimization. The scalability issue addressed by LRF-Dyn is a pressing concern in the field of neural networks, and the article's results have important implications for the development of more sustainable and environmentally friendly AI architectures.
Recommendations
- ✓ Further research is needed to optimize and fine-tune the LRF-Dyn architecture for broader applicability and improved performance.
- ✓ The scalability issue addressed by LRF-Dyn should be explored in the context of other neural network architectures, to better understand the implications for AI development and deployment.
Sources
Original: arXiv - cs.AI
