An Approach to Self- Learning Multicore Reconfiguration Management Applied on Robotic Vision

Walter Stechele; Jan Hartmann; Erik Maehle

doi:10.1109/DASIP.2011.6136882

An Approach to Self- Learning Multicore Reconfiguration Management Applied on Robotic Vision

Walter Stechele, Jan Hartmann, Erik Maehle

Institute of Computer Engineering

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 language	English
Title of host publication	Proceedings of the 2011 Conference on Design & Architectures for Signal & Image Processing (DASIP)
Number of pages	6
Publisher	IEEE
Publication date	01.12.2011
Pages	217-222
Article number	6136882
ISBN (Print)	978-1-4577-0620-2
ISBN (Electronic)	978-1-4577-0621-9, 978-1-4577-0619-6
DOIs	https://doi.org/10.1109/DASIP.2011.6136882
Publication status	Published - 01.12.2011
Event	2011 Conference on Design and Architectures for Signal and Image Processing - Tampere, Finland Duration: 02.11.2011 → 04.11.2011 Conference number: 88850

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title = "An Approach to Self- Learning Multicore Reconfiguration Management Applied on Robotic Vision",

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.",

author = "Walter Stechele and Jan Hartmann and Erik Maehle",

year = "2011",

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series = "IEEE Proceedings",

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note = "2011 Conference on Design and Architectures for Signal and Image Processing <br/>, DASIP 2011 ; Conference date: 02-11-2011 Through 04-11-2011",

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Stechele, W, Hartmann, J & Maehle, E 2011, An Approach to Self- Learning Multicore Reconfiguration Management Applied on Robotic Vision. in Proceedings of the 2011 Conference on Design & Architectures for Signal & Image Processing (DASIP)., 6136882, IEEE Proceedings, IEEE, pp. 217-222, 2011 Conference on Design and Architectures for Signal and Image Processing
, Tampere, Finland, 02.11.11. https://doi.org/10.1109/DASIP.2011.6136882

An Approach to Self- Learning Multicore Reconfiguration Management Applied on Robotic Vision. / Stechele, Walter; Hartmann, Jan; Maehle, Erik.
Proceedings of the 2011 Conference on Design & Architectures for Signal & Image Processing (DASIP). IEEE, 2011. p. 217-222 6136882 (IEEE Proceedings).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

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AB - 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.

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An Approach to Self- Learning Multicore Reconfiguration Management Applied on Robotic Vision

Abstract

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