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Probabilistic Performance Modelling when Using Partial Reconfiguration to Accelerate Streaming Applications with Non-deterministic Task Scheduling

  • Bruno da SilvaEmail author
  • An Braeken
  • Abdellah Touhafi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11444)

Abstract

Many streaming applications composed of multiple tasks self-adapt their tasks’ execution at runtime as response to the processed data. This type of application promises a better solution to context switches at the cost of a non-deterministic task scheduling. Partial reconfiguration is a unique feature of FPGAs that not only offers a higher resource reuse but also performance improvements when properly applied. In this paper, a probabilistic approach is used to estimate the acceleration of streaming applications with unknown task schedule thanks to the application of partial reconfiguration. This novel approach provides insights in the feasible acceleration when partially reconfiguring regions of the FPGA are partially reconfigured in order to exploit the available resources by processing multiple tasks in parallel. Moreover, the impact of how different strategies or heuristics affect to the final performance is included in this analysis. As a result, not only an estimation of the achievable acceleration is obtained, but also a guide at the design stage when searching for the highest performance.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.ETRO DepartmentVrije Universiteit Brussel (VUB)BrusselsBelgium
  2. 2.INDI DepartmentVrije Universiteit Brussel (VUB)BrusselsBelgium

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