An Efficient Data Partitioning Method for Limited Memory Embedded Systems

In this work we propose a framework to carry out an efficient data partitioning for arrays on limited memory embedded systems. We introduce a concept of footprint to precisely calculate the memory demands of references and compute a profit value of a reference using its access frequency and reuse factor. We then develop a methodology based on 0/1 knapsack algorithm to partition the references in local/remote memory. We show the performance improvements due to our approach and compare the results.

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