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A genetic algorithm for scheduling tasks onto dynamically reconfigurable hardware

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A genetic algorithm for scheduling tasks onto dynamically reconfigurable hardware. / Qu, Yang; Soininen, Juha Pekka; Nurmi, Jari.

2007 IEEE International Symposium on Circuits and Systems. 2007. p. 161-164.

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

Harvard

Qu, Y, Soininen, JP & Nurmi, J 2007, A genetic algorithm for scheduling tasks onto dynamically reconfigurable hardware. in 2007 IEEE International Symposium on Circuits and Systems. pp. 161-164, 2007 IEEE International Symposium on Circuits and Systems, ISCAS 2007, New Orleans, LA, United States, 27/05/07. https://doi.org/10.1109/ISCAS.2007.378246

APA

Qu, Y., Soininen, J. P., & Nurmi, J. (2007). A genetic algorithm for scheduling tasks onto dynamically reconfigurable hardware. In 2007 IEEE International Symposium on Circuits and Systems (pp. 161-164) https://doi.org/10.1109/ISCAS.2007.378246

Vancouver

Qu Y, Soininen JP, Nurmi J. A genetic algorithm for scheduling tasks onto dynamically reconfigurable hardware. In 2007 IEEE International Symposium on Circuits and Systems. 2007. p. 161-164 https://doi.org/10.1109/ISCAS.2007.378246

Author

Qu, Yang ; Soininen, Juha Pekka ; Nurmi, Jari. / A genetic algorithm for scheduling tasks onto dynamically reconfigurable hardware. 2007 IEEE International Symposium on Circuits and Systems. 2007. pp. 161-164

Bibtex - Download

@inproceedings{3b6164098c994c14a4443aa01fc58f1f,
title = "A genetic algorithm for scheduling tasks onto dynamically reconfigurable hardware",
abstract = "In this paper, a genetic algorithm (GA) for scheduling tasks onto dynamically reconfigurable devices is presented. The scheduling problem is NP-hard and more complicated than multiprocessor scheduling, because both the task allocation and the configurations need to be carefully managed. The approach has been validated with a number of random task graphs. The results show that the GA approach has good convergence and it is in average 8.6{\%} better than a list-based scheduler for large task graphs of various sizes.",
author = "Yang Qu and Soininen, {Juha Pekka} and Jari Nurmi",
year = "2007",
doi = "10.1109/ISCAS.2007.378246",
language = "English",
pages = "161--164",
booktitle = "2007 IEEE International Symposium on Circuits and Systems",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - A genetic algorithm for scheduling tasks onto dynamically reconfigurable hardware

AU - Qu, Yang

AU - Soininen, Juha Pekka

AU - Nurmi, Jari

PY - 2007

Y1 - 2007

N2 - In this paper, a genetic algorithm (GA) for scheduling tasks onto dynamically reconfigurable devices is presented. The scheduling problem is NP-hard and more complicated than multiprocessor scheduling, because both the task allocation and the configurations need to be carefully managed. The approach has been validated with a number of random task graphs. The results show that the GA approach has good convergence and it is in average 8.6% better than a list-based scheduler for large task graphs of various sizes.

AB - In this paper, a genetic algorithm (GA) for scheduling tasks onto dynamically reconfigurable devices is presented. The scheduling problem is NP-hard and more complicated than multiprocessor scheduling, because both the task allocation and the configurations need to be carefully managed. The approach has been validated with a number of random task graphs. The results show that the GA approach has good convergence and it is in average 8.6% better than a list-based scheduler for large task graphs of various sizes.

U2 - 10.1109/ISCAS.2007.378246

DO - 10.1109/ISCAS.2007.378246

M3 - Conference contribution

SP - 161

EP - 164

BT - 2007 IEEE International Symposium on Circuits and Systems

ER -