Christian Gutsche on behalf of the ATLAS Liquid Argon Calorimeter group Institute of Nuclear and Particle Physics, TU Dresden, Germany Machine Learning for Real-Time Processing of ATLAS Liquid Argon Calorimeter Signals with FPGAs DIS2022: XXIX International Workshop on Deep-Inelastic Scattering and Related Subjects • Sampling calorimeters for the measurement of energy deposited by electrons, photons and hadronic jets • 182 000 cells filled with Liquid Argon as medium for ionization • Resulting triangular pulse is amplified, shaped and digitized at 40 MHz • Energy reconstruction with Optimal Filter (OF): linear combination of up to 5 samples 2. Phase II Readout Electronics Upgrade ●High Luminosity LHC (HL-LHC) planned to start in 2029 with up to 7.5 times nominal luminosity ●About 140 proton-proton-collisions per bunch crossing ●Challenges for LAr calorimeter readout under HL-LHC conditions: ●Revised trigger scheme allows selection of events at 1 MHz and in subsequent bunch crossings (BC) ●Number of events with overlapping signals will increase → in-time and out-of-time pile-up ●New Liquid Argon Signal Processor (LASP) boards will be installed during Long Shutdown 3 (LS3, Phase-II upgrade): ●FPGA to implement more complex real-time energy reconstruction algorithms ●Maximum latency of ~150 ns required by trigger ●Up to 512 detector cells processed on one FPGA ●Readout chain can be simulated with AREUS software: ●Supports electronics noise, digitization and LHC bunch train structure ●Generates digitized pulse sequence as expected from specific detector cell 3.1 Convolutional Neural Networks (CNN) ●Machine Learning solutions under investigation to replace OF in future HL-LHC conditions ●Pulse tagging sub-network (2 layers) ●Sigmoid activation function ●Energy reconstruction sub-network (1-2 layers) ●Uses results of tagging sub-network and raw ADC samples ●ReLU activation function ●Trained and evaluated on AREUS samples ●Training in two stages: 1. Tagging part only as pre-training 2. All layers together for energy reconstruction 0 5 10 15 20 25 30 35 40 BC 1 − 0 1 2 3 4 5 [GeV] T E True energy Digitized signal AREUSSimulation ) = (0.5125, 0.0125) φ , η EMB Middle ( > = 140 µ < 0 1 2 3 4 5 [GeV] T E BC #6 BC #7 BC #8 BC #9 BC #10 RNN ADC(6) RNN ADC(7) ... RNN ADC(10) 0 Dense ET(7) 0 1 2 3 4 5 [GeV] T E BC #7 BC #8 BC #9 BC #10 BC #11 RNN ADC(7) RNN ADC(8) ... RNN ADC(11) 0 Dense ET(8) 0 1 2 3 4 5 [GeV] T E BC #8 BC #9 BC #10 BC #11 BC #12 RNN ADC(8) RNN ADC(9) ... RNN ADC(12) 0 Dense ET (9) 3.2 Recurrent Neural Networks (RNN) RNN cell ADC(n) Dense ET (n − 4) RNN cell ADC(n + 1) Dense ET (n − 3) RNN cell ADC(n + 4) Dense ET (n) RNN cell ADC(n + 5) Dense ET (n + 1) ... ... ... ●RNNs have connections between nodes over time → memory ●Long Short-Term Memory (LSTM) well suited for long sequences due to gated design ●Limited number of internal dimensions and only one layer due to FPGA resource constraints ●Single dense neuron as decoder to reconstruct energy from LSTM output ●Single-cell LSTM (above) ●Operates sample per sample on entire sequence ●Expected to be more robust for overlapping pulses ●Sliding-window LSTM (right) ●Newly instantiated LSTM operates on fixed number of past samples at each BC ●Expected to be more robust for isolated pulses ●Vanilla RNN: simple structure to reconstruct energy and forward information 3.3 Performance in Simulation ●CNNs and RNNs outperform OF in both mean and bias behaviour ●CNNs/RNNs show less biased behaviour with overlapping pulses Figures: difference between predicted and true energy over the temporal gap between two higher energy deposits for OF (right) and and different types of artificial neural networks (ANN) (bottom) 4. FPGA Implementation and Performance ●CNN: Direct implementation in VHDL ●Optimal use of DSP resources on FPGA ●Architecture automatically configured from files obtained during training ●RNN: High Level Synthesis approach ●Easier design and more flexibility in architecture ●Go