literature
Literature
Authors
Jobs
Seminars
Conferences
DataBETA

The BondMachine toolkit: Enabling Machine Learning on FPGA

Oct 31, 2019
12 pages
Published in:
  • PoS ISGC2019 (2019) 020
Contribution to:
  • Published: Oct 31, 2019 by SISSA
DOI:
Experiments:
View in:
pdf
reference search0 citations

Citations per year

0 Citations
Abstract: (SISSA)
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.
  • [1]

    Fast parallel genetic programming: multi-core cpu versus many-core gpu

    • D.M. Chitty
      • Soft Computing 16 (2012) 1795-1814
  • [2]

    Gpgpu: general-purpose computation on graphics hardware

    • D. Luebke
      ,
    • M. Harris
      ,
    • N. Govindaraju
      ,
    • A. Lefohn
      ,
    • M. Houston
    et al.
  • [3]

    The opencl specification

    • A. Munshi
  • [4]

    Fpga programming for the masses

    • D. Bacon
      ,
    • R. Rabbah
      ,
    • S. Shukla
      • Queue 11 (2013) 40-52
  • [5]
    The Go Programming Language Professional, 1st ed
    • A.A. Donovan
      ,
    • B.W. Kernighan
  • [6]

    That ‘internet of things’ thing

    • K. Ashton
      • RFID journal 22 (2009) 97-114
  • [7]

    Machine learning

    • D. Michie
      ,
    • D.J. Spiegelhalter
      ,
    • C. Taylor
  • [9]

    Tensorflow: A system for large-scale machine learning

    • M. Abadi
      ,
    • P. Barham
      ,
    • J. Chen
      ,
    • Z. Chen
      ,
    • A. Davis
    et al.
  • [10]
    Deep Learning with Keras. Packt Publishing Ltd
    • A. Gulli
      ,
    • S. Pal
  • [11]

    Virtual infrastructure management in private and hybrid clouds

    • B. Sotomayor
      ,
    • R.S. Montero
      ,
    • I.M. Llorente
      ,
    • I. Foster
      • IEEE Internet computing 13 (2009) 14-22
  • [12]

    Hybrid computing—where hpc meets grid and cloud computing

    • G. Mateescu
      ,
    • W. Gentzsch
      ,
    • C.J. Ribbens
      • Future Gener.Comput.Syst. 27 (2011) 440-453
  • [13]

    Communicating sequential processes

    • C.A.R. Hoare
  • [14]
    Neural networks: a comprehensive foundation PTR
    • S. Haykin
  • [15]

    Top-down parsing for neural network exchange format (nnef) in tensorflow-based deep learning computation

    • B. Seo
      ,
    • M. Shin
      ,
    • Y.J. Mo
      ,
    • J. Kim
  • [16]

    Bmapi repository.

  • [17]
    The Zynq Book: Embedded Processing with the Arm Cortex-A9 on the Xilinx Zynq-7000 All Programmable Soc. Strathclyde Media
    • L.H. Crockett
      ,
    • R.A. Elliot
      ,
    • M.A. Enderwitz
      ,
    • R.W. Stewart
  • [18]

    O’Reilly Media, Inc.

    • M. Ryan
      ,
    • F. Lucifredi
  • [19]

    Collaboration
    •
    et al.