Sets are all you need: Ultrafast jet classification on FPGAs for HL-LHC

Nature Machine Intelligence Dear Editors, We are hereby submitting the paper'AXXX' to Nature Machine Intelligence as we believe that the content fits the target audience of this Journal and the novelty criteria you require. To our knowledge the present study is the first demonstration of the application of graph neural networks for jet tagging on FPGAs for inference time within O(100) ns. Using the HLS4ML library combined with quantization-aware training and efficient FPGA implementations, we show that O(100) ns inference of complex architectures like graph convolutional neural networks, garnet and interaction networks is feasible at low resource-cost. Our target application is the real-time processing of Large Hadron Collider (LHC) data. However, we believe that the proposed solution could fit other problems related to low latency data selection beyond the LHC. The conditions at the LHC are unique and at the extreme end of the inference-on-the-edge spectrum.