September 15, 2022 Master Thesis Open Access

Greta Heine

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        "affiliation": "KIT/ETP, KIT/IPE", 
        "name": "Greta Heine"
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    "description": "<p>Efficient reconstruction of charged particle trajectories is a crucial yet very difficult step&nbsp;in the analysis pipeline of high energy physics (HEP) experiments. Recent work has&nbsp;shown that graph neural networks (GNNs) are well suited for the pattern recognition&nbsp;task of track finding, where tracking detector hits can be naturally represented as nodes&nbsp;and particle track segments as edges. The interaction network (IN) GNN architecture&nbsp;provides computationally efficient edge classification of the high-dimensional and sparse&nbsp;tracker data, which is especially crucial for implementation in constrained computing&nbsp;environments such as field programmable gate arrays (FPGAs). This work describes the&nbsp;overall workflow for implementing and systematically analyzing an IN-based classification&nbsp;of track segments on FPGAs for the anti-Proton&nbsp;Annihilation at&nbsp;DArmstadt (PANDA)&nbsp;forward tracking system (FTS). This workflow includes data preprocessing, graph building, GNN-based edge classification and a series of FPGA implementation design studies&nbsp;concerning latency, resource utilization, and classification quality using the high level&nbsp;synthesis for machine learning (hls4ml) compiler. The presented final implementation of&nbsp;the GNN-based track segment classifier on a Xilinx Zynq&nbsp;provides an overall inference latency of about&nbsp;0.99&nbsp;&mu;s&nbsp;using about&nbsp;34 %&nbsp;of available digital signal processors (DSPs) and&nbsp;85 %&nbsp;of available lookup tables (LUTs). This work enables the acceleration of charged particle tracking on heterogeneous computational resources toward real-time track reconstruction for the PANDA experiment. The discussed methods and studies could be easily adapted and used in other HEP experiments for accelerated charged particle tracking.</p>", 
    "keywords": [
      "HEP, Machine Learning, GNN, FPGA, PANDA, Tracking"
    ], 
    "publication_date": "2022-09-15", 
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    "resource_type": {
      "subtype": "master-thesis", 
      "title": "Master Thesis", 
      "type": "thesis"
    }, 
    "thesis": {
      "report_number": "ETP-KA/2022-14", 
      "supervisors": [
        {
          "affiliation": "KIT/ETP", 
          "name": "Prof. Dr. Torben Ferber"
        }, 
        {
          "affiliation": "KIT/IPE", 
          "name": "Dr. Michele Caselle"
        }
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    }, 
    "title": "A Fast AI-Based Track Reconstruction on FPGA for the PANDA Experiment"
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