An approach to self-learning multicore reconfiguration management applied on robotic vision

Walter Stechele, Jan Hartmann, Erik Maehle

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Robotic Vision combined with real-time control imposes challenging requirements on embedded computing nodes in robots, exhibiting strong variations in computational load due to dynamically changing activity profiles. Reconfigurable Multiprocessor System-on-Chip offers a solution by efficiently handling the robot's resources, but reconfiguration management seems challenging. The goal of this paper is to present first ideas on self-learning reconfiguration management for Reco nfigurable multicore computing nodes with dynamic reconfiguration of soft-core CPUs and HW accelerators, to support dynamically changing activity profiles in Robotic Vision scenarios.

Original languageEnglish
Title of host publicationProceedings of the 2011 Conference on Design and Architectures for Signal and Image Processing, DASIP 2011
Pages217-222
Number of pages6
DOIs
StatePublished - 2011
Event2011 Conference on Design and Architectures for Signal and Image Processing, DASIP 2011 - Tampere, Finland
Duration: 2 Nov 20114 Nov 2011

Publication series

NameConference on Design and Architectures for Signal and Image Processing, DASIP
ISSN (Print)2164-9766

Conference

Conference2011 Conference on Design and Architectures for Signal and Image Processing, DASIP 2011
Country/TerritoryFinland
CityTampere
Period2/11/114/11/11

Keywords

  • Multicore
  • Reconfiguration
  • Robotic Vision

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