23,595 research outputs found

    Semantic programming model-based design

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    For a generic flexible efficient array antenna receiver platform a hierarchical tiled architecture has been proposed, giving a heterogeneous multi-processor system-on-chip (MPSoC), multiple chips on a board (MCoB) and multiple boards in a system (MBiS). A wide range of MPSoCs are predicted to be used in the near future but how to efficiently apply these designs remains an issue. We will advocate a model-based design approach and propose a single semantic (programming) model for representing the specification, design and implementation and allowing for verification, simulation, architecture definition and design space exploration.\ud \ud A single model for specification, (formal or functional) verification, simulation and programming an MPSoC has obvious as well as some less obvious advantages. It allows for model-based design down to the implementation, especially for hierarchical MPSoC architectures. Partitioning and mapping of the functionality to an architecture is commonly done manually. Using the proposed approach the feasibility of (partly) automated design space exploration is discussed for determining either a partitioning and mapping for a given architecture or an optimal architecture based on set constraints.\ud \ud The proposed hierarchical tiled architecture provides a flexible reconfigurable solution, however partitioning, mapping, modeling and programming such systems remains an issue. The proposed approach tackles these problems at a higher conceptual level, thereby exploiting the inherent composability and parallelism available in the formalism. Design space explorations is facilitated by allowing transformations between different partitionings and mappings. However, the generic applicability and limitations of this approach will need to be researched further.\ud \u

    NoC-based Architectures for Real-Time Applications : Performance Analysis and Design Space Exploration

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    Monoprocessor architectures have reached their limits in regard to the computing power they offer vs the needs of modern systems. Although multicore architectures partially mitigate this limitation and are commonly used nowadays, they usually rely on intrinsically non-scalable buses to interconnect the cores. The manycore paradigm was proposed to tackle the scalability issue of bus-based multicore processors. It can scale up to hundreds of processing elements (PEs) on a single chip, by organizing them into computing tiles (holding one or several PEs). Intercore communication is usually done using a Network-on-Chip (NoC) that consists of interconnected onchip routers allowing communication between tiles. However, manycore architectures raise numerous challenges, particularly for real-time applications. First, NoC-based communication tends to generate complex blocking patterns when congestion occurs, which complicates the analysis, since computing accurate worst-case delays becomes difficult. Second, running many applications on large Systems-on-Chip such as manycore architectures makes system design particularly crucial and complex. On one hand, it complicates Design Space Exploration, as it multiplies the implementation alternatives that will guarantee the desired functionalities. On the other hand, once a hardware architecture is chosen, mapping the tasks of all applications on the platform is a hard problem, and finding an optimal solution in a reasonable amount of time is not always possible. Therefore, our first contributions address the need for computing tight worst-case delay bounds in wormhole NoCs. We first propose a buffer-aware worst-case timing analysis (BATA) to derive upper bounds on the worst-case end-to-end delays of constant-bit rate data flows transmitted over a NoC on a manycore architecture. We then extend BATA to cover a wider range of traffic types, including bursty traffic flows, and heterogeneous architectures. The introduced method is called G-BATA for Graph-based BATA. In addition to covering a wider range of assumptions, G-BATA improves the computation time; thus increases the scalability of the method. In a second part, we develop a method addressing design and mapping for applications with real-time constraints on manycore platforms. It combines model-based engineering tools (TTool) and simulation with our analytical verification technique (G-BATA) and tools (WoPANets) to provide an efficient design space exploration framework. Finally, we validate our contributions on (a) a serie of experiments on a physical platform and (b) two case studies taken from the real world: an autonomous vehicle control application, and a 5G signal decoder applicatio

    Exploring Task Mappings on Heterogeneous MPSoCs using a Bias-Elitist Genetic Algorithm

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    Exploration of task mappings plays a crucial role in achieving high performance in heterogeneous multi-processor system-on-chip (MPSoC) platforms. The problem of optimally mapping a set of tasks onto a set of given heterogeneous processors for maximal throughput has been known, in general, to be NP-complete. The problem is further exacerbated when multiple applications (i.e., bigger task sets) and the communication between tasks are also considered. Previous research has shown that Genetic Algorithms (GA) typically are a good choice to solve this problem when the solution space is relatively small. However, when the size of the problem space increases, classic genetic algorithms still suffer from the problem of long evolution times. To address this problem, this paper proposes a novel bias-elitist genetic algorithm that is guided by domain-specific heuristics to speed up the evolution process. Experimental results reveal that our proposed algorithm is able to handle large scale task mapping problems and produces high-quality mapping solutions in only a short time period.Comment: 9 pages, 11 figures, uses algorithm2e.st
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