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

Murat Isik, Hiruna Vishwamith, Jonathan Naoukin, I. Can Dikmen

TL;DR

Particle identification from detector responses requires capturing temporal dynamics and contextual cues within hardware constraints. HPCNeuroNet merges Spiking Neural Networks with Transformer attention and is implemented on FPGA via the HLS4ML toolchain to enable efficient time-series processing. Across CMS Electron, CMS Proton, and DUNE-like datasets, HPCNeuroNet achieves high accuracy (e.g., 94.48%), low latency (down to 11.5 ms), and favorable resource metrics, outperforming several baseline models. This work demonstrates a scalable, neuromorphic-accelerated approach for real-time high-energy physics analysis on reconfigurable hardware, with potential applications to broader scientific data streams.

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.

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

TL;DR

Particle identification from detector responses requires capturing temporal dynamics and contextual cues within hardware constraints. HPCNeuroNet merges Spiking Neural Networks with Transformer attention and is implemented on FPGA via the HLS4ML toolchain to enable efficient time-series processing. Across CMS Electron, CMS Proton, and DUNE-like datasets, HPCNeuroNet achieves high accuracy (e.g., 94.48%), low latency (down to 11.5 ms), and favorable resource metrics, outperforming several baseline models. This work demonstrates a scalable, neuromorphic-accelerated approach for real-time high-energy physics analysis on reconfigurable hardware, with potential applications to broader scientific data streams.

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.

Paper Structure

This paper contains 15 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Neural Networks in Particle Physics
  3. Proposed Design Methodology
  4. Datasets
  5. CERN CMS Electron Collision Data
  6. CERN DUNE Particle Identification from Detector Responses
  7. CERN CMS Proton Collision Data Observations and Count Models
  8. HPCNeuroNet
  9. Experimental Setup
  10. CPU/GPU Implementation
  11. FPGA Implementation
  12. Evaluation
  13. Conclusions
  14. Future Work
  15. Acknowledgements

Figures (4)

  • Figure 1: The fundamental FPGA architecture.
  • Figure 2: A standard procedure to convert an ML model for digital hardware devices deployment via hls4ml is depicted. The model training and framework, represented by the green boxes (left), are executed in traditional ML software frameworks. The hls4ml setup and transformation processes are demonstrated in the blue boxes (center). The purple boxes (right) highlight potential methods to incorporate the HLS project into a more extensive hardware design.
  • Figure 3: Block Diagram of Implementation
  • Figure 4: Performance of HPCNeuronet across three distinct hardware platforms using various datasets.