## 1 Introduction
The decision will be even more demanding in the HL-LHC phase because of the increased luminosity, the unprecedented radiation dose, and the vast majority of uninteresting additional proton  
Figure 1: CMS detector overview [4]: sub-detector layers are designed to measure particles produced in proton-proton collisions (Layers: 1. Silicon Tracker, 2. Electromagnetic Calorimeter, 3. Hadron Calorimeter, 4. Superconducting Solenoid, 5. Iron return yoke interspersed with Muon chambers). proton interactions referred to as pile-up (PU). Thus, the purpose of this paper is not a physical analysis but the introduction of input data reduction algorithms, as well as the implementation of a real-time model for signal versus background classification. An expected increase in the data rate for the HL-LHC requires advanced approaches that would enable efficient real-time analysis methods for the HGCAL L1 trigger. Artificial intelligence, especially machine learning (ML), has become a standard tool in analyzing big data and is gradually becoming a common tool in HEP. The development of sophisticated ML algorithms, specifically convolutional neural networks (CNN), has been shown as a powerful tool in image recognition, solving many computer vision problems. Until recently, the main drawback of real-time implementation was the fact that running capable ML methods requires time and powerful hardware. Advanced approaches have enabled the integration of ML methods in real-time processing tasks [8; 9; 10]. However, in restricted conditions under which the collection and primary selection of data take place inside the LHC detectors, the standard hardware and operating systems used for typical ML applications are not possible to use. Because of radiation exposure, restricted budget, the possibility of material damage, and the enormous data rate, a choice has been made on field-programmable gate arrays (FPGAs) to be used as a platform for L1 trigger algorithms. Compared to central processing units or graphic processing units, they are very efficient because of very high throughput, low latency, and low power budget. Recently, scientists have begun to explore using ML techniques, specifically neural networks (NN), on FPGAs to improve real-time event processing. This paper deals with the same topic and presents a case study for real-time signal/background classification of the HGCAL data using quantized neural networks deployed on FPGAs. The reason behind network quantization is an effort to reduce the network requirements since limited resources are available on the target FPGA, shared between multiple algorithms. Moreover, input data reduction is applied using selection and quantization procedures to additionally decrease the model size and reduce latency and memory requirements. This paper is organized as follows. Section 2 describes the application of NN in HEP, particularly for triggering purposes in particle detectors. Section 3 presents the methodology of the conducted study: the process of generating the image data set is described, together with details of NN model optimization. The section ends with the introduction to the hls4 ml tool. Section 4 illustrates the performance of the described models via several data refinement approaches and comparisons with baseline particle classification methods. The results of the implementation of selected models in FPGA using hls4 ml are discussed at the end of the section. Section 5 concludes the paper, followed by  
Figure 2: The HGCAL endcap mechanical construction (**left**) and a single electromagnetic and hadronic calorimeter layer (**right**), adjusted from [5; 11]. the references used. Readers should also note that, before the References, a brief Glossary of physics-related terms used throughout this manuscript is included. Understanding these terms is not critical to solving the engineering challenges that the manuscript addresses, but it should aid the interested reader with the manuscript's readability.