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Integer Quantization of Graph Neural Networks for Real-Time FPGA Track Finding
Real-time track finding for displaced-muon signatures in the CMS Level-1 trigger must operate under strict fixed-latency constraints of 12.5 $μ$s while processing high-throughput detector data. Because muon hits map naturally onto sparse, irregular graphs, graph neural networks (GNNs) are attractive
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