Statement-Level Timing Estimation for Embedded System Design Using Machine Learning Techniques

During the initial design phases of an embedded system, the ability to support designers using metrics, obtained through a preliminary analysis, is of fundamental importance. Knowing which initial parameters of the embedded system (HW or SW) influence such metrics is even more important. The main characteristic of an embedded system that typically designers need to measure is the embedded SW (i.e., functions) execution time, used to describe the final system's performance (i.e., timing performance metric). The evaluation of such a metric is often a critical task, relying on several different techniques at different abstraction levels. Furthermore, in the era of Big Data, the use of Machine Learning methods can be a valid alternative to the classic methods used to evaluate or estimate metrics for temporal performance. In such a context, this paper describes a framework, based on the use of Machine Learning methods, to calculate a statement-level embedded software timing performance metric. Results are compared with those obtained with different approaches. They show that the proposed method improves the estimation accuracy for specific processor classes while also reducing estimation time.