Probabilistic Performance Modelling when using Partial Reconfiguration to Accelerate Streaming Applications with Non-Deterministic Task Scheduling

Research output: Chapter in Book/Report/Conference proceedingConference paper

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 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.
Original languageEnglish
Title of host publicationApplied Reconfigurable Computing - 15th International Symposium, ARC 2019, Proceedings
EditorsPedro Diniz, Roger Woods, Christian Hochberger, Andreas Koch, Brent Nelson
Chapter2
Pages81-95
Number of pages15
ISBN (Electronic)978-3-030-17227-5
DOIs
Publication statusPublished - 9 Apr 2019
EventInternational Symposium on Applied Reconfigurable Computing - Darmstadt, Darmstadt, Germany
Duration: 9 Apr 201911 Apr 2019
Conference number: 15
https://www.arc2019.tu-darmstadt.de/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11444 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Symposium on Applied Reconfigurable Computing
Abbreviated titleARC
Country/TerritoryGermany
CityDarmstadt
Period9/04/1911/04/19
Internet address

Keywords

  • Partial Reconfiguration
  • FPGA
  • Probabilistic Performance Model
  • Performance Estimation
  • Streaming Applications

Fingerprint

Dive into the research topics of 'Probabilistic Performance Modelling when using Partial Reconfiguration to Accelerate Streaming Applications with Non-Deterministic Task Scheduling'. Together they form a unique fingerprint.

Cite this