The next generation of embedded software has high performance requirements and is increasingly dynamic. Multiple applications are typically sharing the system, running in parallel in different combinations, starting and stopping their individual execution at different moments in time. The different combinations of applications are forming system execution scenarios. In this paper, we present the distributed application layer, a scenario-based design flow for mapping a set of applications onto heterogeneous on-chip many-core systems. Applications are specified as Kahn process networks and the execution scenarios are combined into a finite state machine. Transitions between scenarios are triggered by behavioral events generated by either running applications or the run-time system. A set of optimal mappings are precalculated during design-time analysis. Later, at runtime, hierarchically organized controllers monitor behavioral events, and apply the precalculated mappings when starting new applications. To handle architectural failures, spare cores are allocated at design-time. At run-time, the controllers have the ability to move all processes assigned to a faulty physical core to a spare core. Finally, we apply the proposed design flow to design and optimize a picture-inpicture software.

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As single-processor systems are ceasing to scale effectively, multi-processor systems are becoming more and more popular. While there are many challenges of designing multi-processor systems in hardware, writing efficient parallel applications that utilize the computing capability of multiple processors may reveal to be even more challenging. In this paper, we introduce a framework that allows to efficiently execute applications expressed as Kahn process networks on multiprocessor systems using protothreads and windowed FIFOs. We show that application developers can use this framework to achieve considerable speed-ups on the Cell Broadband Engine without needing to write architecture-specific code.

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High computational performance in multiprocessor system-on-chips (MPSoCs) is constrained by the ever-increasing power densities in integrated circuits, so that nowadays MPSoCs face various thermal issues. For instance, high chip temperatures may lead to long-term reliability concerns and short-term functional errors. Therefore, the new challenge in designing embedded real-time MPSoCs is to guarantee the final performance and correct function of the system, considering both functional and non-functional properties. One way to achieve this is by ruling out mapping alternatives that do not fulfill requirements on performance or peak temperature already in early design stages. In this article, we propose a thermal-aware optimization framework for mapping real-time applications onto MPSoC platforms. The performance and temperature of mapping candidates are evaluated by formal temporal and thermal analysis models. To this end, analysis models are automatically generated during design space exploration, based on the same specifications as used for software synthesis. The analysis models are automatically calibrated with performance data reflecting the execution of the system on the target platform. The data is automatically obtained prior to design space exploration based on a set of benchmark mappings. Case studies show that the performance and temperature requirements are often conflicting goals and optimizing them together leads to major benefits in terms of a guaranteed and predictable high performance.

Abstract-Due to the trend of many-core systems for dynamic multimedia applications, the problem size of mapping optimization gets bigger than ever making conventional metaheuristics no longer effective. Thus, in this paper, we propose a problem decomposition approach for large scale optimization problems. We basically follow the divide-and-conquer concept, in which a large scale problem is divided into several sub-problems. To remove the inter-relationship between sub-problems, proper abstraction is applied. The divided sub-problems can be solved either in parallel or in a sequence. The mapping optimization problem on dynamic many-core systems is decomposed and solved separately considering the system state and architectural hierarchy. Experimental evaluations with several examples prove that the proposed technique outperforms the conventional metaheuristics both in optimality and diversity of the optimized pareto curve.