PHAT-JeT combines geometric message-passing with hierarchical patch attention to reach state-of-the-art accuracy and background rejection among resource-constrained jet tagging models on four benchmarks.
Hls4ml lhc jet dataset (150 particles)
5 Pith papers cite this work. Polarity classification is still indexing.
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SNAC-Pack automates hardware-aware neural architecture codesign for FPGAs via surrogate-based multi-objective search, QAT/pruning compression, and hls4ml synthesis, yielding compact models with reduced resources on jet classification and qubit readout.
HGQ-LUT delivers a practical LUT-aware training framework with new tensor-based layers, heterogeneous quantization, and a resource surrogate that automates accuracy-efficiency trade-offs for FPGA DNN inference.
SAL-T enhances the linformer with spatially aware kinematic partitioning and convolutions to match full-attention transformer performance on jet tagging while keeping linear complexity and lower latency.
JEDI-linear is a linear-complexity GNN for FPGA jet tagging that reports sub-60 ns latency, higher accuracy than prior designs, and no DSP usage while meeting HL-LHC CMS Level-1 trigger requirements.
citing papers explorer
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Patch Hierarchical Attention Transformer for Efficient Particle Jet Tagging
PHAT-JeT combines geometric message-passing with hierarchical patch attention to reach state-of-the-art accuracy and background rejection among resource-constrained jet tagging models on four benchmarks.
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Surrogate Neural Architecture Codesign Package (SNAC-Pack)
SNAC-Pack automates hardware-aware neural architecture codesign for FPGAs via surrogate-based multi-objective search, QAT/pruning compression, and hls4ml synthesis, yielding compact models with reduced resources on jet classification and qubit readout.
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HGQ-LUT: Fast LUT-Aware Training and Efficient Architectures for DNN Inference
HGQ-LUT delivers a practical LUT-aware training framework with new tensor-based layers, heterogeneous quantization, and a resource surrogate that automates accuracy-efficiency trade-offs for FPGA DNN inference.
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Spatially Aware Linear Transformer (SAL-T) for Particle Jet Tagging
SAL-T enhances the linformer with spatially aware kinematic partitioning and convolutions to match full-attention transformer performance on jet tagging while keeping linear complexity and lower latency.
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JEDI-linear: Fast and Efficient Graph Neural Networks for Jet Tagging on FPGAs
JEDI-linear is a linear-complexity GNN for FPGA jet tagging that reports sub-60 ns latency, higher accuracy than prior designs, and no DSP usage while meeting HL-LHC CMS Level-1 trigger requirements.