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The BondMachine toolkit: Enabling Machine Learning on FPGA

Mirko Mariotti, Loriano Storchi, Daniele Spiga, Davide Salomoni, Tommaso Boccali, Daniele Bonacorsi
Dipartimento di Fisica e Geologia, Universitá degli Studi di Perugia, Via Pascoli, 06123 Perugia, Italy
PoS ISGC2019, 020, 2019

@article{mariotti2019bondmachine,

   title={The BondMachine toolkit: Enabling Machine Learning on FPGA},

   author={Mariotti, Mirko and Storchi, Loriano and Boccali, Tommaso and Salomoni, Davide and Bonacorsi, Daniele and Spiga, Daniele},

   journal={PoS},

   pages={020},

   year={2019},

   publisher={SISSA}

}

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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.
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