LL-GNN: Low Latency Graph Neural Networks on FPGAs for Particle Detectors

  title={LL-GNN: Low Latency Graph Neural Networks on FPGAs for Particle Detectors},
  author={Zhiqiang Que and Marcus Loo and Hongxiang Fan and Michaela Blott and Maurizio Pierini and Alexander Tapper and Wayne Luk},
—This work proposes a novel reconfigurable architecture for low latency Graph Neural Network (GNN) design specifically for particle detectors. Adopting FPGA-based GNNs for particle detectors is challenging since it requires sub-microsecond latency to deploy the networks for online event selection in the Level-1 triggers for the CERN Large Hadron Collider experiments. This paper proposes a custom code transformation with strength reduction for the matrix multiplication operations in the… 
1 Citations

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Reconfigurable Acceleration of Graph Neural Networks for Jet Identification in Particle Physics

  • Zhiqiang QueMarcus LooW. Luk
  • Computer Science
    2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS)
  • 2022
A novel reconfigurable architecture to accelerate Graph Neural Networks (GNNs) for JEDI-net, a jet identification algorithm in particle physics which achieves state-of-the-art accuracy, is presented, which avoids the costly multiplication of the adjacency matrix with the input feature matrix.

Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

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