HPCNeuroNet: A Neuromorphic Approach Merging SNN Temporal Dynamics with Transformer Attention for FPGA-based Particle Physics
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 24 references that are in the map. First posted on arXiv 23 Dec 2024.
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12 of 24 references.
References · 12
- 2023SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networkscited by 88
- 2021hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devicescited by 62
- 2020Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physicscited by 34
- 2023SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event based Recognitioncited by 20
- 2022Spikeformer: A Novel Architecture for Training High-Performance Low-Latency Spiking Neural Networkcited by 19
- 2022Implementing Spiking Neural Networks on Neuromorphic Architectures: A Reviewcited by 15
- 2022Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4mlcited by 14
- 2022Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4mlcited by 11
- 2023A Survey of Spiking Neural Network Accelerator on FPGAcited by 7
- 2023On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experimentscited by 7
- 2022Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physicscited by 5
- 2022A Design Methodology for Fault-Tolerant Computing using Astrocyte Neural Networkscited by 4
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