Recon?gurable architectures are becoming increasingly popular as they bear a promise of combining the ?exibility of software with the performance of hardware. Nevertheless, such architectures are subject to numerous constraints, such as performance, memory requirements, chip area, and power consumption. To create an efficient design, performing Design Space Exploration (DSE) at various stages is essential in order to effectively appraise several design alternatives. DSE at early design stages facilitates designers in rapid performance evaluation of different parameters, such as architectural characteristics, application-to-architecture mappings, scheduling policies, and hardware-software partitionings. DSE methodologies help traversing (typically) huge design spaces efficiently, thus performing DSE at a high level of abstraction facilitates design decisions to be made at very early design stages, which can significantly reduce the overall design time of a system. Towards this goal, in this dissertation, we develop a generic system-level framework, called rSesame, in order to perform modeling and simulation of dynamically recon?gurable architectures at early design stages. The framework can be deployed as a standard modeling and simulation framework for performing system-level DSE to explore several design parameters, while designing dynamically recon?gurable architectures. Performing runtime evaluations together with static explorations, enables recon?gurable architectures to be more ef?cient in terms of several design constraints. As a result, the rSesame framework combines both static and runtime explorations. We deployed the rSesame framework to evaluate the Molen recon?gurable architecture by assessing and evaluating a wide range of application-to-architecture-mappings. These mappings are evaluated based on different in order to facilitate system-level DSE of recon?gurable architectures with respect to architectural exploration, hardware-software partitioning and task mapping/scheduling system attributes, such as execution time, number of recon?gurations, time-weighted area usage, percentage of hardware/software execution, percentage of recon?guration, and hardware reusability ef?ciency, under different resource conditions. The case study shows that the rSesame framework can be efficiently deployed to facilitate system-level DSE of recon?gurable architectures by effectively appraising several alternatives, both statically and at runtime. The study also shows that the framework can be deployed, not only to evaluate and compare different architecture-to-application-mappings, but also to efficiently evaluate different architectural conditions at runtime.