Speaker
Description
The Overlap Muon Track Finder (OMTF) is a key subsystem of the CMS L1 Trigger, and for the CMS phase-2 upgrade during the High-Luminosity Large Hadron Collider era, a new version of the OMTF is being developed. This upgraded version, implemented on a custom ATCA board with a Xilinx UltraScale+ FPGA and 25 Gbps optical transceivers, focuses on improving the muon trigger algorithm and input data pre-processing using High-Level Synthesis (HLS). Furthermore, the potential of Graph Neural Networks (GNNs) is explored to enhance the reconstruction of transverse momentum and position of muons by utilizing the graph structure of the reconstructed stubs from each muon chamber. This aims to improve the accuracy and speed of muon reconstruction while meeting the real-time processing demands of the CMS detector as well as exploring the AI capabilities of the Versal ACAPs. The design, verification results, and experiences in both standard and non-standard HLS workflows, along with a starting point for hardware testing of GNN models on FPGAs, are presented.
