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.
LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
Physics-informed GNNs with four detector-aware graph constructions and a custom message passing layer achieve MAE 0.8525 for pT estimation on CMS trigger data with over 55% fewer parameters than baselines.
citing papers explorer
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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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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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Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems
Physics-informed GNNs with four detector-aware graph constructions and a custom message passing layer achieve MAE 0.8525 for pT estimation on CMS trigger data with over 55% fewer parameters than baselines.