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Description
With increasing beam background levels at Belle II, which have already been observed due to the world-record instantaneous luminosities achieved by SuperKEKB and which are expected to rise further, an upgrade of the current Level 1 (L1) trigger algorithms is necessary to handle the evolving conditions. In this work, we present an upgraded L1 electromagnetic calorimeter trigger, based on Graph Neural Networks (GNNs) using dynamic graph building, implemented on the AMD XCVU160 FPGA used in the Belle II Universal Trigger Board 4 (UT4). The algorithm was developed in a software-hardware codesign approach, including quantization-aware training, pruning, and post-training optimizations, having both performance optimization and hardware requirements in mind. The network performs cluster finding and reconstruction in a one-shot approach, without assuming a predefined maximum number of clusters. We demonstrate an implementation of a 15-layer deep GNN with multiple graph construction and message passing steps on the FPGA. This design achieves the required throughput of 8 MHz and an overall latency of 3 $\mu$s. The implementation on the UT4 was deployed in monitoring mode within the full Belle II L1 trigger system and was included in collision data-taking in December 2024. This is the first operation of a GNN-based hardware trigger. We report implementation results showing 75% logic block usage and full utilization of DSP resources, with validation on both collision and cosmic-ray data collected with the Belle II detector.
