A distributed arithmetic algorithm for CMVM operations on FPGAs reduces area by up to one third and latency for quantized neural networks, integrated into hls4ml.
The Phase-2 Upgrade of the CMS Level-1 Trigger,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2025 2verdicts
UNVERDICTED 2representative citing papers
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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da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAs
A distributed arithmetic algorithm for CMVM operations on FPGAs reduces area by up to one third and latency for quantized neural networks, integrated into hls4ml.
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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.