Distributed Memory Allocation Technique for Synchronous Dataflow Graphs

Karol Desnos (1) , Maxime Pelcat (1) , Jean-François Nezan (1) , Slaheddine Aridhi (2)

1 IETR - Institut d'Électronique et des Technologies du numéRique
2 TI - Texas Insruments

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%.

Domaines: Systèmes embarqués; Calcul parallèle, distribué et partagé [cs.DC]; Traitement du signal et de l'image [eess.SP]

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, IEEE; IEEE Signal Processing Society; IEEE CAS, Oct 2016, Dallas, TX, United States. ⟨10.1109/SiPS.2016.16⟩. ⟨hal-01390486⟩