将脉冲神经网络与注意力机制结合，提升粒子物理中粒子识别的精度。
HPCNeuroNet: A Neuromorphic Approach Merging SNN Temporal Dynamics with Transformer Attention for FPGA-based Particle Physics
- 融合脉冲网络时序特性与Transformer注意力机制
- 在FPGA上部署，实现高能物理数据高效处理
- 适合需要低延迟、高能效的粒子探测场景
本文提出HPCNeuroNet模型，首次将脉冲神经网络（SNNs）的时间动态性与Transformer的注意力机制相结合，用于高能物理中的粒子识别任务。该模型通过HLS4ML框架实现，并针对FPGA平台进行优化，充分发挥其高能效与低延迟优势。在典型粒子探测数据集上的实验表明，该模型在区分不同粒子类型时表现出更优的准确率和可扩展性。相比传统机器学习方法，HPCNeuroNet在保持低功耗的同时显著提升了对复杂探测信号的建模能力，展示了神经形态计算在高能物理领域的革新潜力。
原文摘要 · Abstract (English)
This paper presents the innovative HPCNeuroNet model, a pioneering fusion of Spiking Neural Networks (SNNs), Transformers, and high-performance computing tailored for particle physics, particularly in particle identification from detector responses. Our approach leverages SNNs' intrinsic temporal dynamics and Transformers' robust attention mechanisms to enhance performance when discerning intricate particle interactions. At the heart of HPCNeuroNet lies the integration of the sequential dynamism inherent in SNNs with the context-aware attention capabilities of Transformers, enabling the model to precisely decode and interpret complex detector data. HPCNeuroNet is realized through the HLS4ML framework and optimized for deployment in FPGA environments. The model accuracy and scalability are also enhanced by this architectural choice. Benchmarked against machine learning models, HPCNeuroNet showcases better performance metrics, underlining its transformative potential in high-energy physics. We demonstrate that the combination of SNNs, Transformers, and FPGA-based high-performance computing in particle physics signifies a significant step forward and provides a strong foundation for future research.
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