Computing in cyber-physical systems (CPS) 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 energy efficiency and worst-case execution time (WCET) minimization.
In the first part, we will explain how the energy consumption of computing in CPS can be reduced with scratch pad memories (SPMs) and with graphic processing units (GPUs). SPMs and GPUs also help us to meet real-time constraints. We will then look at real-time constraints more closely and consider WCETs minimization. We do this by integrating compilers and WCET estimation. We will demonstrate how such an integration opens the door to WCET-reduction algorithms. For example, an algorithm for mapping frequently accessed memory objects to 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 in cyber-physical systems. We will demonstrate our approach for a flexible error handling in the presence of real-time constraints which are possibly prohibiting time consuming error corrections.