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  • 标题:The BondMachine toolkit: Enabling Machine Learning on FPGA
  • 本地全文:下载
  • 作者:M.Mariotti ; L.Storchi ; D.Spiga
  • 期刊名称:PoS - Proceedings of Science
  • 印刷版ISSN:1824-8039
  • 出版年度:2019
  • 卷号:351
  • DOI:10.22323/1.351.0020
  • 语种:English
  • 出版社:SISSA, Scuola Internazionale Superiore di Studi Avanzati
  • 摘要: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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