Embedded Neural Networks on FPGAs for Real-Time Computation of the Energy Deposited in the ATLAS Liquid Argon Calorimeter

At the HL-LHC, the number of proton-proton collisions in one bunch-crossing (called pileup) increases significantly, putting more stringent requirements on the LHC detectors electronics and real-time data-processing capabilities. The ATLAS LAr calorimeter measures with an excellent resolution the energy of particles produced in LHC collisions. The energy is computed in real-time using optimal filtering (OF) algorithms running on dedicated data-acquisition electronic boards based on FPGAs. However, with the increased pileup, the performance of these algorithms decreases significantly. Dedicated Neural networks (NNs) are found to outperform the OF algorithms. The architecture, performance, and firmware implementation for these NNs will be presented.