Machine learning for real-time processing of ATLAS liquid argon calorimeter signals with FPGAs
Year: 2025
Type: article
Abstract: To deal with the higher pileup after the high-luminosity upgrade of the LHC, the ATLAS liquid argon calorimeter readout electronics will be replaced. 556 Intel Agilex-7 FPGAs will be installed as part of the off-detector electronics, enabling a more sophisticated digital energy reconstruction. Instead of using a linear optimal filter like the current readout, we evaluated the use of recurrent and convolutional neural networks. These artificial neural networks are trained on signal-enriched simulated detector pulses for a set of representative cells. The network size is limited to approximately 500 multiply-accumulate operations per detector cell. They show improvements in energy reconstruction especially for the case of overlapping signal pulses, as they will appear more often under high-luminosity conditions. An FPGA prototype firmware has been developed for both RNNs and CNNs, that supports processing the required number of 384 detector cells on one FPGA. This uses a low-level VHDL approach to minimize the resource usage. The ANN architecture is fixed at compile time, but the weights are configurable per cell. The compilation results show that such a neural network based readout is feasible with the available hardware. This is possible through the use of time multiplexing, i.e. by running the FPGA at a multiple of the 40 MHz input data frequency.
Source: Journal of Instrumentation
Authors: Johann Christoph Voigt, the ATLAS Liquid Argon Calorimeter group
Institution: Technische Universität Dresden
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,290
Awards: G1190173965, G1572746594, Artificial intelligence on FPGAs: a breakthrough for data acquisition in high energy physics experiments and beyond, G4435500008, FIS-Projekt 05H2018 - Fortentwicklung des ATLAS-Experiments zum Einsatz am HL-LHC: Phase-II-Upgrade der ATLAS LAr-Kalorimeter-Elektronik, G5296751580, G5742446233, Verbundprojekt 05H2024 - Fortentwicklung des ATLAS-Experiments zum Einsatz am HL-LHC: Phase-II Upgrade der Ausleseelektronik des ATLAS Liquid-Argon-Kalorimeters, G6159765138
OpenAlex ID: W4407224673