Abstract
Water Cherenkov detectors have been widely adopted as a low-cost technique for cosmic rays (CR) studies. Thus, an existing CR readout system has been chosen as the base DAQ (data acquisition) design, which has been paired to a Neural Network (NN) in order to work as a trace/event discrimination block. We present the compression of two NN architectures for particle classification, targeting a low-end System-on-Chip (SoC). The hls4ml package is used to translate the final NN models into a high-level synthesis project. Both NNs were implemented and tested on Xilinx SoC ZC7Z020 Zynq. A comparison of the accuracy of the detection, resource utilization and latency of the two NNs are presented. The results show the benefits of using compression techniques to deploy a reduced model, which provides a good compromise between efficiency, effectiveness, latency, as well as resource utilization.
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Molina, R.S. et al. (2022). Compression of NN-Based Pulse-Shape Discriminators in Front-End Electronics for Particle Detection. In: Saponara, S., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2021. Lecture Notes in Electrical Engineering, vol 866. Springer, Cham. https://doi.org/10.1007/978-3-030-95498-7_13
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DOI: https://doi.org/10.1007/978-3-030-95498-7_13
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