Systematic Methodology for Real-Time Cost-Effective Mapping of Dynamic Concurrent Task-Based Systems on Heterogenous Platforms

Systematic Methodology for Real-Time Cost-Effective Mapping of Dynamic Concurrent Task-Based Systems on Heterogeneous Platforms gives an overview of the state-of-the-art in system-level design trade-off explorations for concurrent tasks running on embedded heterogeneous multiple processors. The targeted application domain covers complex embedded real-time multi-media and communication applications. Many of these applications are concurrent in the sense that multiple subsystems can be running simultaneously. Also, these applications are so dynamic at run-time that the designs based on the worst case execution times are inefficient in terms of resource allocation (e.g., energy budgets). A novel systematical approach is clearly necessary in the area of system-level design for the embedded systems where those concurrent and dynamic applications are mapped. This material is mainly based on research at IMEC and its international university network partners in this area in the period 1997-2006. In order to deal with the concurrent and dynamic behaviors in an energy-performance optimal way, we have adopted a hierarchical system model (i.e., the gray-box model) that can both exhibit the sufficient detail of the applications for design-time analysis and hide unnecessary detail for a low-overhead run-time management. We have also developed a well-balanced design-time/run-time combined task scheduling methodology to explore the trade-off space at design-time and efficiently handle the system adaptations at run-time. Moreover, we have identified the connection between task-level memory/communication management and task scheduling and illustrated how to perform the task-level memory/communication management in order to obtain the design constraints that enable the this connection. A fast approach is also shown to estimate at the system-level, the energy and performance characterization of applications executing on the target platform processors.