EPJ Web Conf.
Volume 245, 202024th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|Number of page(s)||8|
|Section||1 - Online and Real-time Computing|
|Published online||16 November 2020|
Fast inference using FPGAs for DUNE data reconstruction
CERN, Genève, Switzerland
* e-mail: email@example.com
Published online: 16 November 2020
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.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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