Exploring accelerated machine learning for experiment data analytics

Project goal

The project has two threads, each investigating a unique use case for the Micron Deep Learning Accelerator (a modular FPGA-based architecture). The first thread relates to the development of a real-time streaming machine inference engine prototype for the level-1 trigger of the CMS experiment.

The second thread focuses on prototyping a particle-identification system based on deep learning for the DUNE experiment. DUNE is a leading-edge, international experiment for neutrino science and proton-decay studies. It will be built in the US and is scheduled to begin operation towards the end of this decade.

R&D topic
Machine learning and data analytics
Project coordinator(s)
Emilio Meschi, Paola Sala, Maria Girone
Team members
Thomas Owen James, Dejan Golubovic, Maurizio Pierini, Manuel Jesus Rodriguez, Saul Alonso-Monsalve, Ema Puljak, Lorenzo Uboldi
Collaborator liaison(s)
Mark Hur, Stuart Grime, Michael Glapa, Eugenio Culurciello, Andre Chang, Marko Vitez, Dustin Werran, Aliasger Zaidy, Abhishek Chaurasia, Patrick Estep, Jason Adlard, Steve Pawlowski


Project background

The level-1 trigger of the CMS experiment selects relevant particle-collision events for further study, while rejecting 99.75% of collisions. This decision must be made with a fixed latency of a few microseconds. Machine-learning inference in FPGAs may be used to improve the capabilities of this system.

The DUNE experiment will consist of large arrays of sensors exposed to high-intensity neutrino beams. The use of convolutional neural networks has been shown to substantially boost particle-identification performance for such detectors. For DUNE, an FPGA solution is advantageous for processing ~ 5 TB/s of data.

Recent progress

The CMS team primarily focussed on preparing a level-1 scouting system using the Micron SB-852 FPGA processing boards to capture and process trigger-level data at 40 MHz. We developed and optimised neural networks to improve analysis performance using level-1 scouting objects. In addition, we developed a system for level-1 anomaly detection using a variational auto-encoder approach. We implemented this using the Micron deep-learning framework.

The DUNE team organised a test on the protoDUNE Single Phase detector to analyse data from cosmic rays. This was the last chance to test it before protoDUNE’s second run in 2022. The test aimed to examine the incoming triggered data using a triple AC-511 Micron FPGA as a hardware accelerator. The hardware ran an image-segmentation neural network designed to identify regions of interest. This setup was able to analyse data at a rate of ~1.42 Gb/s. The capability of the network to identify low-energy events was tested in data taken by protoDUNE with a neutron generator.

Next steps

The CMS team will focus on completing the installation and validation of the 40 MHz scouting system for LHC Run 3. This will require the integration and debugging of several software, FPGA-firmware, and hardware layers. The performance of a deep-learning-driven anomaly-detection algorithm will also be evaluated for use in LHC Run 3.

The DUNE team plans to continue analysing the neutron generator's data, as it demonstrates huge potential for the reconstruction techniques at DUNE. The goal is to perform studies in real time to understand the quality of the data taken using Micron's state-of-the-art hardware accelerators for the data-acquisition system of protoDUNE.


    D. Golubovic, 40 MHz scouting with deep learning in CMS. Published on Zenodo, 2020. cern.ch/go/vJD9
    M. Popa, Deep learning for 40 MHz scouting with level-1 trigger muons for CMS at LHC run-3. Published on Zenodo, 2020. cern.ch/go/99St


    M. J. R. Alonso, Fast inference using FPGAs food DUNE data reconstruction (7 November). Presented at 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, 2019. cern.ch/go/bl7n
    M. J. R. Alonso, Prototyping of a DL-based Particle Identification System for the Dune Neutrino Detector (22 January). Presented at CERN openlab Technical Workshop, Geneva, 2020. cern.ch/go/zH8W
    T. O. James, FPGA-based Machine Learning Inference for CMS with the Micron Deep Learning Accelerator (22 January). Presented at CERN openlab Technical Workshop, Geneva, 2020. cern.ch/go/pM7P
    M. Rodríguez, S. A. Monsalve, P. Sala, Prototyping of a DL-based Particle Identification System for the Dune Neutrino Detector (22 January). Presented at CERN openlab Technical Workshop, Geneva, 2020. cern.ch/go/zH8W
    D. Golubovic, T. James, E. Meschi, 40 MHz Scouting with Deep Learning in CMS (22-24 April). Presented at Connecting the Dots Workshop, New Jersey, 2020. cern.ch/go/C96N