Toward Relative Positional Encoding in Spiking Transformers

• URL: http://arxiv.org/abs/2501.16745v1
• Date: Tue, 28 Jan 2025 06:42:37 GMT
• Title: Toward Relative Positional Encoding in Spiking Transformers
• Authors: Changze Lv, Yansen Wang, Dongqi Han, Yifei Shen, Xiaoqing Zheng, Xuanjing Huang, Dongsheng Li,
• Abstract summary: Spiking neural networks (SNNs) are bio-inspired networks that model how neurons in the brain communicate through discrete spikes. In this paper, we introduce an approximate method for relative positional encoding (RPE) in Spiking Transformers.
• Score: 52.62008099390541
• License:
• Abstract: Spiking neural networks (SNNs) are bio-inspired networks that model how neurons in the brain communicate through discrete spikes, which have great potential in various tasks due to their energy efficiency and temporal processing capabilities. SNNs with self-attention mechanisms (Spiking Transformers) have recently shown great advancements in various tasks such as sequential modeling and image classifications. However, integrating positional information, which is essential for capturing sequential relationships in data, remains a challenge in Spiking Transformers. In this paper, we introduce an approximate method for relative positional encoding (RPE) in Spiking Transformers, leveraging Gray Code as the foundation for our approach. We provide comprehensive proof of the method's effectiveness in partially capturing relative positional information for sequential tasks. Additionally, we extend our RPE approach by adapting it to a two-dimensional form suitable for image patch processing. We evaluate the proposed RPE methods on several tasks, including time series forecasting, text classification, and patch-based image classification. Our experimental results demonstrate that the incorporation of RPE significantly enhances performance by effectively capturing relative positional information.

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