Publications (8)
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jenni Ott, Shiva Abbaszadeh +118
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environmen…
Ultrafast jet classification on FPGAs for the HL-LHC
Patrick Odagiu, Zhiqiang Que, Javier Duarte +13
Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, w…
LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics
Zhiqiang Que, Hongxiang Fan, Marcus Loo +5
This work presents a novel reconfigurable architecture for Low Latency Graph Neural Network (LL-GNN) designs for particle detectors, delivering unprecedented low latency performanc…
JEDI-linear: Fast and Efficient Graph Neural Networks for Jet Tagging on FPGAs
Zhiqiang Que, Chang Sun, Sudarshan Paramesvaran +8
Graph Neural Networks (GNNs), particularly Interaction Networks (INs), have shown exceptional performance for jet tagging at the CERN High-Luminosity Large Hadron Collider (HL-LHC)…
Sub-microsecond Transformers for Jet Tagging on FPGAs
Lauri Laatu, Chang Sun, Arianna Cox +7
We present the first sub-microsecond transformer implementation on an FPGA achieving competitive performance for state-of-the-art high-energy physics benchmarks. Transformers have…
JetFormer: A Scalable and Efficient Transformer for Jet Tagging from Offline Analysis to FPGA Triggers
Ruoqing Zheng, Chang Sun, Qibin Liu +7
We present JetFormer, a versatile and scalable encoder-only Transformer architecture for particle jet tagging at the Large Hadron Collider (LHC). Unlike prior approaches that are o…