Abstract
The Deep Underground Neutrino Experiment (DUNE) will be a world-class neutrino observatory and nucleon decay detector aiming to address some of the most fundamental questions in particle physics. With a modular liquid argon time-projection chamber (LArTPC) of 40 kt fiducial mass, the DUNE far detector will be able to reconstruct neutrino interactions with an unprecedented resolution. With no triggering and no zero suppression or compression, the total raw data volume would be of order 145 EB/year. Consequently, fast and affordable reconstruction methods are needed. Several state-of-theart methods are focused on machine learning (ML) approaches to identify the signal within the raw data or to classify the neutrino interaction during the reconstruction. One of the main advantages of using those techniques is that they will reduce the computational cost and time compared to classical strategies. Our plan aims to go a bit further and test the implementation of those techniques on an accelerator board. In this work, we present the accelerator board used, a commercial off-the-shelf (COTS) hardware for fast deep learning (DL) inference based on an FPGA, and the experimental results obtained outperforming more traditional processing units. The FPGA-based approach is planned to be eventually used for online reconstruction.
Highlights
The Deep Underground Neutrino Experiment (DUNE) will be an international neutrino observatory designed to answer fundamental questions about the nature of elementary particles and their role in the universe [1]
The far detector (FD) will be composed of four liquid argon time-projection chambers (LArTPC) each of them with a total fiducial mass of 10 kt
There is a growing demand for computing resources needed by modern machine learning (ML) methods; hardware accelerators have entered in place
Summary
The Deep Underground Neutrino Experiment (DUNE) will be an international neutrino observatory designed to answer fundamental questions about the nature of elementary particles and their role in the universe [1]. The DUNE far detector (FD) will be located about 1.5 km underground at the Sanford Underground Research Facility (SURF) in South Dakota, US, at a distance of 1300 km from Fermilab where the world’s most intense neutrino beam will target the FD. The FD will be composed of four liquid argon time-projection chambers (LArTPC) each of them with a total fiducial mass of 10 kt. The liquid-argon technology allows us to reconstruct neutrino interactions with image-like precision and unprecedented resolution. The data acquisition (DAQ) system for the DUNE FD gathers beam-related interactions, as well as cosmic-ray muons and atmospheric neutrino interactions; added together, recording their activity will dominate the data rate. The data rate for each 10-kt module is expected to be as much as 1.5 TB/s. The ultimate limit on the output data rate of. Standard computing infrastructure, i.e., CPUs, is usually not suitable for this ever-increasing technology, so other concrete solutions are needed
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