Bondhugula U.

Bruno Girodias

Coppola M.

Dick R. P.

El Mostapha Abouhamid

Gabriela Nicolescu

Luiza Gheorghe Iugan

Markus S.

Michel Langevin

Pasricha S.

Pierre Paulin

Tang L.

Youcef Bouchebaba

Due to their great ability to parallelize at a very high integration level, Multi-Processors Systems-on-Chip (MPSoCs) are good candidates for systems and applications such as multimedia. Memory is becoming a key player for significant improvements in these applications (power, performance and area). The large amount of data manipulated by these applications requires high-capacity computing and memory. Lately, new programming models have been introduced. This leads to the need of new optimization and mapping techniques suitable for embedded systems and their programming models. This article presents novel approaches for combining memory optimization with mapping of data-driven applications while considering anti-dependence conflicts. Two different approaches are studied and integrated with existing mapping algorithms. The first approach (based on heuristic algorithms) keeps the graph transformation for memory optimization stage from the mapping stage and enables their combination in a design flow. The second approach (based on evolutionary algorithms) combines these two stages and integrates them in a unique stage. Some significant improvements are obtained for memory gain, communication load and physical links.</jats:p

Crossref

ACM Transactions on Embedded Computing Systems

Integrating Memory Optimization with Mapping Algorithms for Multi-Processors System-on-Chip

Girodias, Bruno

Iugan, Luiza Gheorghe

Bouchebaba, Youcef

Nicolescu, Gabriela

Abouhamid, El Mostapha

Langevin, Michel

Paulin, Pierre

PolyPublie

Integrating memory optimization with mapping algorithms for multi-processors system-on-chip

Integrating Memory Optimization with Mapping Algorithms for Multi-Processors System-on-Chip

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PolyPublie