Indico has been upgraded to version 3.1. Details in the SSB
Nov 4 – 8, 2019
Adelaide Convention Centre
Australia/Adelaide timezone

Fast inference using FPGAs for DUNE data reconstruction

Nov 7, 2019, 11:45 AM
Riverbank R5 (Adelaide Convention Centre)

Riverbank R5

Adelaide Convention Centre

Oral Track 1 – Online and Real-time Computing Track 1 – Online and Real-time Computing


Manuel Jesus Rodriguez Alonso (CERN)


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 raw data volume for four modules would be of order 145 EB/year. Consequently, fast and affordable reconstruction methods are needed. Several state-of-the-art 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 inference based on an FPGA, and the experimental results obtained outperforming more traditional processing units.

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Primary authors


Paola Sala (CERN and INFN Milano)

Presentation materials