Embedded streaming applications specified using parallel Models of Computation (MoC) often contain ample amount of parallelism which can be exploited using Multi-Processor System-on-Chip (MPSoC) platforms. It has been shown that the various forms of parallelism in an application should be explored to achieve the maximum system performance. However, if more parallelism is revealed than needed, it will overload the underlying MPSoC platform. At the same time, the revealed parallelism should be sufficient such that the MPSoC platform is fully utilized. Therefore, the amount of revealed and exploited parallelism has to be justenough with respect to the platform constraints. In this paper, we study the problem of exploiting just-enough parallelism by application task unfolding, when mapping streaming applications modeled using the Synchronous Data Flow (SDF) MoC onto MPSoC platforms in hard real-time systems. We show that our problem of simultaneously unfolding and allocating tasks under hard real-time scheduling has a bounded solution space and derive its upper bounds. Subsequently, we devise an efficient algorithm to solve the problem, while the obtained solution meets a pre-specified quality. The experiments on a set of real-life streaming applications demonstrate that our algorithm results, within reasonable amount of time, in a system specification with large performance gain. Finally, we show that our proposed algorithm is on average 100 times faster than one of the state-of-the-art meta-heuristics, i.e., NSGA-II genetic algorithm, while achieving the same quality of solutions.

In real-time systems, the application's behavior has to be predictable at compile-time to guarantee timing constraints. However, modern streaming applications which exhibit adaptive behavior due to mode switching at run-time, may degrade system predictability due to unknown behavior of the application during mode transitions. Therefore, proper temporal analysis during mode transitions is imperative to preserve system predictability. To this end, in this paper, we initially introduce Mode Aware Data Flow (MADF) which is our new predictable Model of Computation (MoC) to efficiently capture the behavior of adaptive streaming applications. Then, as an important part of the operational semantics of MADF, we propose the Maximum-Overlap Offset (MOO) which is our novel protocol for mode transitions. The main advantage of this transition protocol is that, in contrast to self-timed transition protocols, it avoids timing interference between modes upon mode transitions. As a result, any mode transition can be analyzed independently from the mode transitions that occurred in the past. Based on this transition protocol, we propose a hard real-time analysis as well to guarantee timing constraints by avoiding processor overloading during mode transitions. Therefore, using this protocol, we can derive a lower bound and an upper bound on the earliest starting time of the tasks in the new mode during mode transitions in such a way that hard real-time constraints are respected.

Abstract-In this paper, we study the problem of minimizing the number of processors required for scheduling latencyconstrained streaming applications modeled as CSDF graphs, where the actors of a CSDF are executed as strictly periodic tasks. We formalize the problem and prove that due to the strict periodicity of actors the problem is an integer convex programming problem, that can be solved efficiently by using an existing convex programming solver. We evaluate our solution approach on a set of 13 real-life streaming applications modeled as CSDF graphs and demonstrate that it can reduce the number of processors in more than 52% of the conducted experiments in comparison to an existing approach. I. IntroductionStreaming applications, such as video/audio processing and digital signal processing, have become prevalent in embedded systems. These applications contain ample amount of parallelism which perfectly matches the processing power of MultiProcessor System-on-Chip (MPSoC) platforms. To efficiently program MPSoC platforms, Models-of-Computation (MoCs) are used to specify streaming applications. Prominent examples of MoCs include Synchronous Data Flow (SDF) [1] and its generalization Cyclo-Static Dataflow (CSDF) [2], in which actors representing computation are executed concurrently, thereby naturally exposing parallelism. Furthermore, the strong design-time analyzability of these MoCs makes them suitable for designing performance-constrained embedded systems.Performance constraints of a streaming application are usually imposed on two principle metrics, throughput and latency. For many streaming applications, the latency is the main concern, where latency is the elapsed time between the arrival of a sample to an application and the output of the processed sample by the application. For instance, in video conferencing and automatic pattern recognition applications, a latency that exceeds a certain limit cannot be tolerated. At the same time, the required number of processors to execute the application should be minimized for better resource usage and energy efficiency.A few efforts have been made to deal with latency of streaming applications specified as SDF/CSDF graphs. The authors in [3] studied minimizing latency for SDF graphs, where latency is computed by a state-space traversal which has exponential complexity. Moreover, they assumed that there is no constraint on the number of processors required to schedule an application. However, the number of processors is an important design concern for embedded systems with respect to power consumption and area. In another effort, the authors in [4] and [5] proposed a scheduling framework that schedules acyclic CSDF graphs in a strictly periodic fashion. In this scheduling framework, each CSDF actor executes strictly periodically and meets a given deadline. The periodic execution of actors guarantees a certain

Streaming applications often require a parallel Model of Computation (MoC) to specify their application behavior and to facilitate mapping onto Multi-Processor System-on-Chip (MPSoC) platforms. Various performance requirements and resource budgets of embedded systems ask for an efficient design space exploration (DSE) approach to select the best design from a design space consisting of a large number of design choices. However, existing DSE approaches explore the design space that includes only architecture and mapping alternatives for an initial application specification given by the application designer. In this article, we first show that a design often might not be optimal if alternative specifications of a given application are not taken into account. We further argue that the best alternative specification consists of only independent and load-balanced application tasks. Based on the Polyhedral Process Network (PPN) MoC, we present an approach to analyze and transform an initial PPN to an alternative one that contains only independent processes if possible. Finally, by prototyping real-life applications on both FPGA-based MPSoCs and desktop multi-core platforms, we demonstrate that mapping the alternative application specification results in a large performance gain compared to those approaches, in which alternative application specifications are not taken into account. ACM Reference Format:Zhai, J. T., Nikolov, H., and Stefanov, T. 2013. Mapping of streaming applications considering alternative application specifications.

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