Constraint satisfaction and optimization techniques are commonly employed in scheduling problems, industrial manufacturing, and automation processes. Constraint Satisfaction Problem (CSP) also finds use in the design, synthesis, and optimization of embedded systems. In recent years online constraint solving techniques have been employed in embedded systems for dynamic system adaptation. In embedded systems, online constraint solving techniques are primarily used as on-board control software. Using CSP techniques for scheduling algorithms provides intelligent scheduling. This thesis discusses the architecture of an embedded, parallel finite-domain constraint solver for performing online constraint satisfaction. By modeling the scheduling problem as a CSP problem, the embedded system becomes adaptable to dynamic changes in the environment. The features of this solver are that it is implemented in a platform with multiple soft-core processors with distributed memory architecture. A tool is also developed that automates the partitioning of the given application and configures the underlying framework.
| Identifer | oai:union.ndltd.org:UTAHS/oai:digitalcommons.usu.edu:etd-1098 |
| Date | 01 May 2008 |
| Creators | Subramanian, Prasad |
| Publisher | DigitalCommons@USU |
| Source Sets | Utah State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | All Graduate Theses and Dissertations |
| Rights | Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact Andrew Wesolek (andrew.wesolek@usu.edu). |
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