Date of Award
Master of Science (MS)
Electrical and Computer Engineering
Due to technology downscaling, embedded systems have increased in complexity and heterogeneity. The increasingly large process, voltage, and temperature variations negatively affect the design and optimization process of these systems. These factors contribute to increased uncertainties that in turn undermine the accuracy and effectiveness of traditional design approaches. In this thesis, we formulate the problem of uncertainty aware mapping for multicore embedded system platforms as a multi-objective optimization problem. We present a solution to this problem that integrates uncertainty models as a new design methodology constructed with Monte Carlo and evolutionary algorithms. The solution is uncertainty aware because it is able to model uncertainties in design parameters and to identify robust design points that limit the influence of these uncertainties onto the objective functions. The proposed design methodology is implemented as a tool that can generate the robust Pareto frontier in the objective space formed by reliability, performance, and energy consumption.