Real-valued black-box optimization of badly behaved and not well understood functions is a wide topic in many scientific areas. Possible applications range from maximizing portfolio profits in financial mathematics over efficient training of neuronal networks in computational linguistics to parameter identification of metabolism models in industrial biotechnology. This paper presents a comparison of several global as well as local optimization strategies applied to the task of efficiently identifying free parameters of a metabolic network model. A focus is being set on the ease of adopting these strategies to modern, highly parallel architectures. Finally an outlook on the possible parallel performance is being presented.

This work investigates the interrelation of performance and robustness against variability in industrial microprocessor designs. A novel analysis technique for variation-sensitive hardware and two figures of merit to quantify the robustness of a design against variations are proposed. Together with a multi-stage STA this enables an efficient application of low-V T cell insertion and pulsed latch design to compensate for within-die delay variations. For the same speed margin of 5% on design level, a pulsed latch design of an ARM926 microprocessor shows a 2.5× higher robustness compared to a MS-FF design with selective low-V T cell insertion.