Memory Analysis and Optimized Allocation of Dataflow Applications on Shared-Memory MPSoCs: In-Depth Study of a Computer Vision Application

Abstract : The majority of applications, ranging from the low complexity to very multifaceted entities requiring dedicated hardware accelerators, are very well suited for Multiprocessor Systems-on-Chips (MPSoCs). It is critical to understand the general characteristics of a given embedded application: its behavior and its requirements in terms of MPSoC resources.This paper presents a complete method to study the important aspect of memory characteristic of an application. This method spans the theoretical, architecture-independent memory characterization to the quasi optimal static memory allocation of an application on a real shared-memory MPSoC. The application is modeled as an Synchronous Dataflow (SDF) graph which is used to derive a Memory Exclusion Graph (MEG) essential for the analysis and allocation techniques. Practical considerations, such as cache coherence and memory broad-casting, are extensively treated. Memory footprint optimization is demonstrated using the example of a stereo matching algorithm from the computer vision domain. Experimental results show a reduction of the memory footprint by up to 43% compared to a state-of-the-art minimization technique, a throughput improvement of 33% over dynamic allocation, and the introduction of a tradeoff between multi-core scheduling flexibility and memory footprint.
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Dernière modification le : jeudi 7 février 2019 - 17:50:22
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Karol Desnos, Maxime Pelcat, Jean-François Nezan, Slaheddine Aridhi. Memory Analysis and Optimized Allocation of Dataflow Applications on Shared-Memory MPSoCs: In-Depth Study of a Computer Vision Application. Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, Springer, 2014, pp.1-19. 〈http://link.springer.com/article/10.1007/s11265-014-0952-6〉. 〈10.1007/s11265-014-0952-6〉. 〈hal-01083576v2〉

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