Open Access

RRES: A Novel Approach to the Partitioning Problem for a Typical Subset of System Graphs

EURASIP Journal on Embedded Systems20072008:259686

Received: 11 May 2007

Accepted: 4 December 2007

Published: 16 December 2007


The research field of system partitioning in modern electronic system design started to find strong advertence of scientists about fifteen years ago. Since a multitude of formulations for the partitioning problem exist, the same multitude could be found in the number of strategies that address this problem. Their feasibility is highly dependent on the platform abstraction and the degree of realism that it features. This work originated from the intention to identify the most mature and powerful approaches for system partitioning in order to integrate them into a consistent design framework for wireless embedded systems. Within this publication, a thorough characterisation of graph properties typical for task graphs in the field of wireless embedded system design has been undertaken and has led to the development of an entirely new approach for the system partitioning problem. The restricted range exhaustive search algorithm is introduced and compared to popular and well-reputed heuristic techniques based on tabu search, genetic algorithm, and the global criticality/local phase algorithm. It proves superior performance for a set of system graphs featuring specific properties found in human-made task graphs, since it exploits their typical characteristics such as locality, sparsity, and their degree of parallelism.

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Authors’ Affiliations

Institute of Communications and Radio-Frequency Engineering, Faculty of Electrical Engineering and Information Technology, Vienna University of Technology


© B. Knerr et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.