Development of a resource-efficient FPGA-based neural network regression model for the ATLAS muon trigger upgrades

Abstract

This paper reports on the development of a resource-efficient FPGA-based neural network regression model for potential applications in the future hardware muon trigger system of the ATLAS experiment at the Large Hadron Collider (LHC). Effective real-time selection of muon candidates is the cornerstone of the ATLAS physics programme. With the planned ATLAS upgrades for the High Luminosity LHC, an entirely new FPGA-based hardware muon trigger system will be installed that will process full muon detector data within a 10 $\mu s$ latency window. The large FPGA devices planned for this upgrade should have sufficient spare resources to allow deployment of machine learning methods for improving identification of muon candidates and searching for new exotic particles. Our neural network regression model promises to improve rejection of the dominant source of background trigger events in the central detector region, which are due to muon candidates with low transverse momenta. This model was implemented in FPGA using 157 digital signal processors and about 5,000 lookup tables. The simulated network latency and deadtime are 122 and 25 ns, respectively, when implemented in the FPGA device using a 320 MHz clock frequency. Two other FPGA implementations were also developed to study the impact of design choices on resource utilisation and latency. The performance parameters of our FPGA implementation are well within the requirements of the future muon trigger system, therefore opening a possibility for deploying machine learning methods for future data taking by the ATLAS experiment.

arXiv comment13 pages, 17 figures

References in corpus (21)

• Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
• Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
• Machine learning and the physical sciences
• Fast inference of deep neural networks in FPGAs for particle physics
• Long-Lived Particles at the Energy Frontier: The MATHUSLA Physics Case
• Performance of the ATLAS muon triggers in Run 2
• Searching for long-lived particles beyond the Standard Model at the Large Hadron Collider
• Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors
• Search for heavy charged long-lived particles in the ATLAS detector in 36.1 fb${}^{-1}$ of proton-proton collision data at $\sqrt{s}=13$ TeV
• Variational Autoencoders for New Physics Mining at the Large Hadron Collider
• Applications and Techniques for Fast Machine Learning in Science
• Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider
• FPGA-accelerated machine learning inference as a service for particle physics computing
• Measurements of the electron and muon inclusive cross-sections in proton-proton collisions at sqrt(s) = 7 TeV with the ATLAS detector
• Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics
• Triggering on Emerging Jets
• A Comparative Study between HLS and HDL on SoC for Image Processing Applications
• Implementation of high-performance, sub-microsecond deep neural networks on FPGAs for trigger applications
• Performance of the ATLAS RPC detector and Level-1 muon barrel trigger at $\sqrt{s}=13$ TeV
• Fast Muon Tracking with Machine Learning Implemented in FPGA
• Fast convolutional neural networks for identifying long-lived particles in a high-granularity calorimeter

Cited by in corpus (3)

• Nanosecond machine learning regression with deep boosted decision trees in FPGA for high energy physics
• Firmware implementation of a recurrent neural network for the computation of the energy deposited in the liquid argon calorimeter of the ATLAS experiment
• Nanosecond hardware regression trees in FPGA at the LHC