Optimizing Graph Neural Networks for Jet Tagging in Particle Physics on FPGAs

Author:

Que Zhiqiang1,Loo Marcus1,Fan Hongxiang1,Pierini Maurizio2,Tapper Alexander3,Luk Wayne1

Affiliation:

1. Imperial College,Department of Computing,London,UK

2. European Organization for Nuclear Research (CERN),Geneva,Switzerland

3. Imperial College,Department of Physics,London,UK

Funder

United King-dom EPSRC

Publisher

IEEE

Reference28 articles.

1. Fast inference of deep neural networks in FPGAs for particle physics

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

3. JEDI-net: a jet identification algorithm based on interaction networks

4. The Phase-2 Upgrade of the CMS Level-1 Trigger;Collaboration,2020

5. Interaction networks for learning about objects, relations and physics;Battaglia;Advances in neural information processing systems,2016

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1. Top-philic machine learning;The European Physical Journal Special Topics;2024-07-25

2. Low Latency Variational Autoencoder on FPGAs;IEEE Journal on Emerging and Selected Topics in Circuits and Systems;2024-06

3. LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics;ACM Transactions on Embedded Computing Systems;2024-03-18

4. ParticleNet for Jet Tagging in Particle Physics on FPGA;Communications in Computer and Information Science;2024

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