Memory Usage Estimation for Dataflow-Model-Based Software Development Methodology

Abstract

In designing software on an embedded hardware platform with limited memory size, it is critical to guarantee that the peak memory requirement does not exceed the memory capacity. We propose a memory usage estimation technique in a model-based software development methodology where the application code is synthesized from a dataflow model-based specification. To minimize the degree of overestimation without running the synthesized software, the proposed technique analyzes the binary file and shared library dependencies of dataflow tasks. We verify the proposed technique in developing real-life applications on Linux and bare-metal systems.