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The BondMachine toolkit: Enabling Machine Learning on FPGA
The BondMachine (BM) is an innovative prototype software ecosystem aimed at creating facilities where both hardware and software are co-designed, guaranteeing a full exploitation of fabric capabilities (both in terms of concurrency and heterogeneity) with the smallest possible power dissipation. In...
Autores principales: | , , , , , |
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Lenguaje: | eng |
Publicado: |
SISSA
2019
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.22323/1.351.0020 http://cds.cern.ch/record/2708682 |
_version_ | 1780965074945441792 |
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author | Mariotti, Mirko Storchi, Loriano Spiga, Daniele Salomoni, Davide Boccali, Tommaso Bonacorsi, Daniele |
author_facet | Mariotti, Mirko Storchi, Loriano Spiga, Daniele Salomoni, Davide Boccali, Tommaso Bonacorsi, Daniele |
author_sort | Mariotti, Mirko |
collection | CERN |
description | The BondMachine (BM) is an innovative prototype software ecosystem aimed at creating facilities where both hardware and software are co-designed, guaranteeing a full exploitation of fabric capabilities (both in terms of concurrency and heterogeneity) with the smallest possible power dissipation. In the present paper we will provide a technical overview of the key aspects of the BondMachine toolkit, highlighting the advancements brought about by the porting of Go code in hardware. We will then show a cloud-based BM as a Service deployment. Finally, we will focus on TensorFlow, and in this context we will show how we plan to benchmark the system with a ML tracking reconstruction from pp collision at the LHC. |
id | oai-inspirehep.net-1766692 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2019 |
publisher | SISSA |
record_format | invenio |
spelling | oai-inspirehep.net-17666922022-08-10T12:24:47Zdoi:10.22323/1.351.0020http://cds.cern.ch/record/2708682engMariotti, MirkoStorchi, LorianoSpiga, DanieleSalomoni, DavideBoccali, TommasoBonacorsi, DanieleThe BondMachine toolkit: Enabling Machine Learning on FPGAComputing and ComputersThe BondMachine (BM) is an innovative prototype software ecosystem aimed at creating facilities where both hardware and software are co-designed, guaranteeing a full exploitation of fabric capabilities (both in terms of concurrency and heterogeneity) with the smallest possible power dissipation. In the present paper we will provide a technical overview of the key aspects of the BondMachine toolkit, highlighting the advancements brought about by the porting of Go code in hardware. We will then show a cloud-based BM as a Service deployment. Finally, we will focus on TensorFlow, and in this context we will show how we plan to benchmark the system with a ML tracking reconstruction from pp collision at the LHC.SISSAoai:inspirehep.net:17666922019 |
spellingShingle | Computing and Computers Mariotti, Mirko Storchi, Loriano Spiga, Daniele Salomoni, Davide Boccali, Tommaso Bonacorsi, Daniele The BondMachine toolkit: Enabling Machine Learning on FPGA |
title | The BondMachine toolkit: Enabling Machine Learning on FPGA |
title_full | The BondMachine toolkit: Enabling Machine Learning on FPGA |
title_fullStr | The BondMachine toolkit: Enabling Machine Learning on FPGA |
title_full_unstemmed | The BondMachine toolkit: Enabling Machine Learning on FPGA |
title_short | The BondMachine toolkit: Enabling Machine Learning on FPGA |
title_sort | bondmachine toolkit: enabling machine learning on fpga |
topic | Computing and Computers |
url | https://dx.doi.org/10.22323/1.351.0020 http://cds.cern.ch/record/2708682 |
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