{"id":153043,"date":"2006-12-01T00:00:00","date_gmt":"2006-12-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/resource-modeling-and-scheduling-for-extensible-embedded-platforms\/"},"modified":"2018-10-16T20:36:50","modified_gmt":"2018-10-17T03:36:50","slug":"resource-modeling-and-scheduling-for-extensible-embedded-platforms","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/resource-modeling-and-scheduling-for-extensible-embedded-platforms\/","title":{"rendered":"Resource Modeling and Scheduling for Extensible Embedded Platforms"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Modern embedded processors have the flexibility of dynamic switching between power operation modes, such as using voltage and frequency scaling. Platforms with heterogeneous processors and reconfigurable buses further extend the energy\/timing trade-off flexibility and provide the opportunity to fine tune resource usage for particular applications. This paper gives a resource model for heterogeneous multi-processor embedded platforms and formulates power-aware real-time resource scheduling problems as integer linear programming problems. In particular, we take the time and energy costs of mode switching into account, which considerably improves the accuracy of the model. We apply the resource model to a stackable multiprocessor embedded platform, called mPlatform, and present a case study of scheduling a sound source localization application in a stack of four MSP430-based sensing boards and one ARM7- based processing board.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Modern embedded processors have the flexibility of dynamic switching between power operation modes, such as using voltage and frequency scaling. Platforms with heterogeneous processors and reconfigurable buses further extend the energy\/timing trade-off flexibility and provide the opportunity to fine tune resource usage for particular applications. This paper gives a resource model for heterogeneous multi-processor embedded 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