A Multi-Core FPGA-based 2D-Clustering Algorithm for High-Throughput Data Intensive Applications

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A multi-core FPGA-based clustering algorithm for high-throughput data intensive applications is presented. The algorithm is optimized for data with two dimensional organization (e.g. image processing, tracking devices for high energy physics detectors etc.) and uses a moving window of generic size t...

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Detalles Bibliográficos
Autores principales: Sotiropoulou, C-L, Annovi, A, Beretta, M, Luciano, P, Nikolaidis, S
Lenguaje:eng
Publicado: 2013
Materias:
Detectors and Experimental Techniques
Acceso en línea:http://cds.cern.ch/record/1600755
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author Sotiropoulou, C-L
Annovi, A
Beretta, M
Luciano, P
Nikolaidis, S
author_facet Sotiropoulou, C-L
Annovi, A
Beretta, M
Luciano, P
Nikolaidis, S
author_sort Sotiropoulou, C-L
collection CERN
description A multi-core FPGA-based clustering algorithm for high-throughput data intensive applications is presented. The algorithm is optimized for data with two dimensional organization (e.g. image processing, tracking devices for high energy physics detectors etc.) and uses a moving window of generic size to adjust to the application’s processing requirements (the cluster sizes and shapes that appear in the input data sets). One or more windows (cores) can be used to identify clusters in parallel, allowing for versatility to increase performance or reduce the amount of used resources. In addition to the inherent parallelism the algorithm is executed in a pipeline, thus allowing for readout to be performed without imposing extra combinatorial delays.
id cern-1600755
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2013
record_format invenio
spelling cern-16007552019-09-30T06:29:59Zhttp://cds.cern.ch/record/1600755engSotiropoulou, C-LAnnovi, ABeretta, MLuciano, PNikolaidis, SA Multi-Core FPGA-based 2D-Clustering Algorithm for High-Throughput Data Intensive ApplicationsDetectors and Experimental TechniquesA multi-core FPGA-based clustering algorithm for high-throughput data intensive applications is presented. The algorithm is optimized for data with two dimensional organization (e.g. image processing, tracking devices for high energy physics detectors etc.) and uses a moving window of generic size to adjust to the application’s processing requirements (the cluster sizes and shapes that appear in the input data sets). One or more windows (cores) can be used to identify clusters in parallel, allowing for versatility to increase performance or reduce the amount of used resources. In addition to the inherent parallelism the algorithm is executed in a pipeline, thus allowing for readout to be performed without imposing extra combinatorial delays.ATL-DAQ-SLIDE-2013-602oai:cds.cern.ch:16007552013-09-16
spellingShingle Detectors and Experimental Techniques
Sotiropoulou, C-L
Annovi, A
Beretta, M
Luciano, P
Nikolaidis, S
A Multi-Core FPGA-based 2D-Clustering Algorithm for High-Throughput Data Intensive Applications
title A Multi-Core FPGA-based 2D-Clustering Algorithm for High-Throughput Data Intensive Applications
title_full A Multi-Core FPGA-based 2D-Clustering Algorithm for High-Throughput Data Intensive Applications
title_fullStr A Multi-Core FPGA-based 2D-Clustering Algorithm for High-Throughput Data Intensive Applications
title_full_unstemmed A Multi-Core FPGA-based 2D-Clustering Algorithm for High-Throughput Data Intensive Applications
title_short A Multi-Core FPGA-based 2D-Clustering Algorithm for High-Throughput Data Intensive Applications
title_sort multi-core fpga-based 2d-clustering algorithm for high-throughput data intensive applications
topic Detectors and Experimental Techniques
url http://cds.cern.ch/record/1600755
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