Embedded Systems, ubiquitous computing, and networked architectures are research areas in computer science where the features of organic computing become increasingly essential. Such features, like self organization and self configuration need to be combined with the still increasing requirement for computing performance. Adaptive computing systems are a promising completion to classical computer architectures. Adaptive Computing Systems (ACS) offers the opportunity to adapt the whole architecture or parts of the architecture to the changing needs of applications or changing environments. Reconfigurable Logic (RL) can itself contain Mono- or Multiprocessors or it can be a component in such systems or computer clusters. All levels of parallelism can be combined with all levels of reconfigurability. Configuration and concurrency offer a large design space to be explored. They become tightly correlated issues in modern adaptive computer architectures. Different grains of configurability bring the flexibility into architectures. This flexibility is necessary to achieve a better exploitation of parallelism in algorithms. Architectures can thus be adapred to all the needs of problems or algorithms to turn the inherent or explicit parallelism into efficiency.

The paper addresses some of these aspects and presents some ideas for modelling and classifying adaptive computing systems (ACS) on different levels of granularity.