Reducing memory requirements of resource-constrained applications

Embedded computing platforms are often resource constrained, requiring great design and implementation attention to memory-power-, and heat-related parameters. An important task for a compiler in such platforms is to simplify the process of developing applications for limited memory devices and resource-constrained clients. Focusing on array-intensive embedded applications to be executed on single CPU-based architectures, this work explores how loop-based compiler optimizations can be used for increasing memory location reuse. Our goal is to transform a given application in such a way that the resulting code has fewer cases (as compared to the original code), where the lifetimes of array elements overlap. The reduction in lifetimes of array elements can then be exploited by reusing memory locations as much as possible. Our experimental results indicate that the proposed strategy reduces data space requirements of 15 resource constrained applications by more than 40%, on average. We also demonstrate how this strategy can be combined with data locality (cache behavior) - enhancing techniques so that a compiler can take advantage of both, that is, reduce data memory requirements and improve data locality at the same time.