Distributed Memory Allocation Technique for Synchronous Dataflow Graphs

Abstract : This paper introduces a new distributed memory allocation technique for applications modeled with Synchronous Dataflow (SDF) graphs. This technique builds on a State-of-the-Art shared memory allocation technique based on a weighted graph, called Memory Exclusion Graph (MEG). A MEG captures the memory reuse opportunities between memory objects that must be allocated before the execution of an SDF graph. The algorithms detailed in this paper enable a single MEG to be split into separate MEGs, each of which is associated with a memory bank accessible only by one core of the architecture. The proposed technique is implemented within a rapid prototyping framework and is evaluated by deploying real computer vision applications on a Multiprocessor System-on-Chip (MPSoC). Results show a systematic performance improvement due to better memory usage, with application speedups ranging from 2% up to 380%.
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Communication dans un congrès
2016 IEEE International Workshop on Signal Processing Systems, Oct 2016, Dallas, TX, United States. 2016 IEEE International Workshop on Signal Processing Systems, Proceedings of,, 2016, 〈10.1109/SiPS.2016.16〉
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Karol Desnos, Maxime Pelcat, Jean-François Nezan, Slaheddine Aridhi. Distributed Memory Allocation Technique for Synchronous Dataflow Graphs. 2016 IEEE International Workshop on Signal Processing Systems, Oct 2016, Dallas, TX, United States. 2016 IEEE International Workshop on Signal Processing Systems, Proceedings of,, 2016, 〈10.1109/SiPS.2016.16〉. 〈hal-01390486〉

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