HPCNeuroNet: A Neuromorphic Approach Merging SNN Temporal Dynamics with Transformer Attention for FPGA-based Particle Physics

TL;DR

HPCNeuroNet combines Spiking Neural Networks and Transformers within FPGA-based high-performance computing to improve particle identification accuracy and scalability in high-energy physics detector data analysis.

Contribution

The paper introduces HPCNeuroNet, a novel fusion of SNNs and Transformers optimized for FPGA deployment, advancing particle physics data analysis.

Findings

HPCNeuroNet outperforms traditional machine learning models in accuracy.

The model demonstrates high scalability and efficiency on FPGA hardware.

Integration of SNNs and Transformers enhances temporal and contextual data interpretation.

Abstract

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.