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

arXiv: https://arxiv.org/abs/2412.17571 · HTML: https://mlsanity.com/paper/2412.17571/

## 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.

## Builds on (most cited first, 12 of 12)

- [SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks](https://mlsanity.com/paper/2302.13939/) (2023)
- [hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices](https://mlsanity.com/paper/2103.05579/) (2021)
- [Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics](https://mlsanity.com/paper/2008.03601/) (2020)
- [SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event based Recognition](https://mlsanity.com/paper/2308.04369/) (2023)
- [Spikeformer: A Novel Architecture for Training High-Performance Low-Latency Spiking Neural Network](https://mlsanity.com/paper/2211.10686/) (2022)
- [Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review](https://mlsanity.com/paper/2202.08897/) (2022)
- [Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml](https://mlsanity.com/paper/2207.00559/) (2022)
- [Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml](https://mlsanity.com/paper/2205.07690/) (2022)
- [A Survey of Spiking Neural Network Accelerator on FPGA](https://mlsanity.com/paper/2307.03910/) (2023)
- [On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments](https://mlsanity.com/paper/2307.11242/) (2023)
- [Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics](https://mlsanity.com/paper/2209.10481/) (2022)
- [A Design Methodology for Fault-Tolerant Computing using Astrocyte Neural Networks](https://mlsanity.com/paper/2204.02942/) (2022)

## 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.