Current multi-core design methodologies are facing increasing unpredictability in terms of quality due to the actual diversity of the workloads that characterize the deployment scenario. To this end, these systems expose a set of dynamic parameters which can be tuned at run-time to achieve a specified Quality of Service (QoS) in terms of performance. A run-time manager operating system module is in charge of matching the specified QoS with the available platform resources by manipulating the overall degree of task-level parallelism of each application as well as the frequency of operation of each of the system cores.In this paper, we introduce a design space exploration framework for enabling and supporting enhanced resource management through software re-configuration on an industrial multicore platform. From one side, the framework operates at design time to identify a set of promising operating points which represent the optimal trade-off in terms of the target power consumption and performance. The operating points are used after the system has been deployed to support an enhanced resource management policy. This is done by a light-weight resource management layer which filters and selects the optimal parallelism of each application and operating frequency of each core to achieve the QoS constraints imposed by the external world and/or the user.We show how the proposed design-time and run-time techniques can be used to optimally manage the resources of a multiple-stream MPEG4 encoding chip dedicated to automotive cognitive safety tasks 1 .

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