Hovedsiden > The BondMachine toolkit: Enabling Machine Learning on FPGA |
Published Articles | |
Title | The BondMachine toolkit: Enabling Machine Learning on FPGA |
Author(s) | Mariotti, Mirko (U. Perugia (main) ; INFN, Perugia) ; Storchi, Loriano (INFN, Perugia ; U. Pescara (main)) ; Spiga, Daniele (INFN, Perugia) ; Salomoni, Davide (INFN, CNAF) ; Boccali, Tommaso (INFN, Pisa) ; Bonacorsi, Daniele (Bologna U.) |
Publication | SISSA, 2019 |
Number of pages | 12 |
In: | PoS ISGC2019 (2019) 020 |
In: | International Symposium on Grids & Clouds 2019, Taipei, Taiwan, 31 Mar - 5 Apr 2019, pp.020 |
DOI | 10.22323/1.351.0020 |
Subject category | Computing and Computers |
Accelerator/Facility, Experiment | CERN LHC |
Abstract | 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. |
Copyright/License | publication: © Authors (License: CC-BY-NC-ND-4.0) |