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. 1 Introduction Embedded systems are often characterized by limited resources. Due to limited local resources, client-server based embedded systems are becoming a popular architecture in which the limited resources of the client are augmented with the server resources. Memory on embedded systems (such as TMS320C2x series) is especially limited due to its high power consumption [11] . On the other hand, most of the applications (such as image processing codes, data compression /uncompression..