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Cited article:

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Lorenzetti Showers - A general-purpose framework for supporting signal reconstruction and triggering with calorimeters

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Computer Physics Communications 286 108671 (2023)
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Firmware implementation of a recurrent neural network for the computation of the energy deposited in the liquid argon calorimeter of the ATLAS experiment

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Journal of Instrumentation 18 (05) P05017 (2023)
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Applications and Techniques for Fast Machine Learning in Science

Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bähr, Jürgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomás E. Müller Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Dongning Guo, et al.
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Artificial Neural Networks on FPGAs for Real-Time Energy Reconstruction of the ATLAS LAr Calorimeters

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Computing and Software for Big Science 5 (1) (2021)
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Online energy reconstruction for calorimeters under high pile-up conditions using deconvolutional techniques

J.P.B.S. Duarte, L.M. de Andrade Filho, E.F. de Simas Filho, P.C.M.A. Farias and J.M. de Seixas
Journal of Instrumentation 14 (12) P12017 (2019)
https://doi.org/10.1088/1748-0221/14/12/P12017