Computing in cyber-physical systems has to reflect the context of the computations and, hence, has to be efficient in terms of a number of objectives. In particular, computing has to be (worst and average case) execution-time and energy efficient, while also being reliable. In this talk, we will consider optimization techniques targeting worst-case execution time (WCET) and energy efficiency.

In the first part, we will consider real-time constraints and WCETs. We do this by integrating compilers and WCET estimation. We will demonstrate how such integration opens the door to WCET-reduction algorithms. For example, an algorithm for mapping frequently accessed memory objects to scratch pad memories (SPMs) is able to reduce the WCET for an automotive application by about 50%. The need to seriously consider WCETs and time constraints also has an impact on applicable error correction techniques. We will demonstrate our approach for a flexible error handling in the presence of real-time constraints possibly prohibiting time consuming error corrections.

In the talk, we will also address the energy consumption of computing in cyber-physical systems. We will explain how scratch pad memories can be used to reduce the energy consumption of such systems. Also, we will demonstrate that graphic processing units (GPUs) provide another way of saving energy. The talk will finish with some remarks on cyber-physical system education.