Compiler-based WCET prediction performing function specialization

The Worst-Case Execution Time (WCET) is one of the most important criteria of hard real-time systems. Many optimizations have been proposed to improve WCET of an embedded application at compile time. Moreover, since modern embedded systems must also satisfy the additional design criteria like, e.g., code size or energy consumption, more often the compiler's optimizations go towards multi-objective optimization problems. Evolutionary algorithms are the most widely used method to solve a multi-objective problem. In order to find the set of the best trade-offs between the objectives, any evolutionary algorithm requires extensive evaluations of the objective functions. Thus, considering WCET as an objective in a multi-objective problem is infeasible in many cases, because the WCET analysis at compile time can be very time-consuming. For this reason, we propose a method based on a machine learning technique to predict the values of WCET at compile time. A well-known compiler-based optimization, function specialization, is considered as a base for the proposed prediction model. A regression method is analyzed in terms of making WCET predictions as precise as possible performing function specialization.