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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abbon, P., et al.: The COMPASS experiment at CERN. Nucl. Instrum. Meth. Phys. Res. Sect. A Accel. Spectrom. Detect. Assoc. Equip. 577, 455–518 (2007)
Sidelnik, I., Asorey, H.: LAGO: the Latin American giant observatory. Nucl. Instrum. Meth. Phys. Res. Sect. A Accel. Spectrom. Detect. Assoc. Equip. 876, 173–175 (2017)
Mace, E.K., Ward, J.D., Aalseth, C.E.: Use of neural networks to analyze pulse shape data in low-background detectors. J. Radioanal. Nucl. Chem. 318(1), 117–124 (2018). https://doi.org/10.1007/s10967-018-5983-1
Holl, P., Hauertmann, L., Majorovits, B., Schulz, O., Schuster, M., Zsigmond, A.J.: Deep learning based pulse shape discrimination for germanium detectors. Eur. Phys. J. C 79(6), 450 (2019). https://doi.org/10.1140/epjc/s10052-019-6869-2
Droz, D., Tykhonov, A., Wu, X.: Neural networks for electron identification with DAMPE. In: Proceedings of 36th International Cosmic Ray Conference (2019)
Garcia, L.G., et al.: Muon–electron pulse shape discrimination for water Cherenkov detectors based on FPGA/SoC. Electronics 10(3), 224 (2021)
Choudhary, T., Mishra, V., Goswami, A., Sarangapani, J.: A comprehensive survey on model compression and acceleration. Artif. Intel. Rev. 53(7), 5113–5155 (2020)
Hinton, G.E., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. In: arXiv preprint arXiv:1503.02531 (2015)
Duarte, J., et al.: Fast inference of deep neural networks in FPGAs for particle physics. J. Instrum. 13(07), P07027–P07027 (2018)
Coelho, C.N., et al.: Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors. Nat. Mach. Intell. 3, 675–686 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-95498-7_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-95497-0
Online ISBN: 978-3-030-95498-7
eBook Packages: EngineeringEngineering (R0)