A formal approach to robustness maximization of complex heterogeneous embedded systems

Embedded system optimization typically considers objectives such as cost, timing, buffer sizes and power consumption. Robustness criteria, i.e. sensitivity of the system to variations of properties like execution and transmission delays, input data rates, CPU clock rates, etc., has found less attention despite its practical relevance. In this paper we introduce robustness metrics and propose an algorithm considering these metrics in design space exploration and system optimization. The algorithm can optimize for static and for dynamic robustness, the latter including system or designer reactions to property variations. We explain several applications ranging from platform optimization to critical component identification. By means of extensive experiments we show that design space exploration pursuing classical design goals does not necessarily yield robust systems, and that our method leads to systems with significantly higher design robustness.