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Efficient Ensemble Machine Learning Implementation on FPGA Using Partial Reconfiguration

  • Gian Carlo Cardarilli
  • Luca Di NunzioEmail author
  • Rocco Fazzolari
  • Daniele Giardino
  • Marco Matta
  • Marco Re
  • Francesca Silvestri
  • Sergio Spanò
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 573)

Abstract

Ensemble Machine Learning (EML) consists of the combination of multiple Artificial Intelligence algorithms. This paper presents an efficient FPGA implementation of an Ensemble based on Long Short-Term Memory Networks (LSTM). For an efficient implementation, the proposed design uses the Partial Reconfiguration function available for FPGAs. Results are presented in terms of resources utilization, reconfiguration speed, power consumption and maximum clock frequency.

Notes

Acknowledgements

The authors would like to thank Xilinx Inc, for providing FPGA hardware and software tools by Xilinx University Program.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gian Carlo Cardarilli
    • 1
  • Luca Di Nunzio
    • 1
    Email author
  • Rocco Fazzolari
    • 1
  • Daniele Giardino
    • 1
  • Marco Matta
    • 1
  • Marco Re
    • 1
  • Francesca Silvestri
    • 1
  • Sergio Spanò
    • 1
  1. 1.University of Rome Tor VergataRomeItaly

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