Digital On-Demand Computing Organism: Stability and Robustness

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An intrinsic feature of many biological systems is their capabilities of self-healing, self-adapting, selfconfiguring etc, or short, self-x features. In contrast, today¿s computing systems hardly feature any of these characteristics even though they promise to raise computing to a new level of applicability. Our proposed approach to organic computing is tightly bound to basic self-x mechanisms as found, for example, in a human body. Starting with investigating basic biological mechanisms, we eventually derive a digital, on-demand computing organism representing the three levels, ¿brain¿, `organ¿ and `cell¿. The ¿on-demand¿ characteristic thereby emphasizes its responsiveness to environmental requests/changes as well as to changes resulting from the dynamics of the computing organism itself. Beginning with the brain level, a Software architecture for a robot controller with emphasis an self-x features is proposed. It closely interacts with an organic middleware at the organ level, featuring a decentralized control loop using messengers. At the cell level, a novel adaptive and dynamically reconfigurable hardware architecture is capable to implement the self-x features in an efficient way. In between, a power management system¿s architecture co-ordinates brain level and cell level for ultralow power system efficiency. All levels are supplied with monitoring techniques and architectures as a prerequisite for enabling self-x features. We believe that our comprehensive approach to organic computing will represent the first step towards more adaptive, more power efficient and more flexible future embedded real-time systems. The proposed project is comprised of five research groups and a neurophysiologic expert: Prof. Becker (hardware architectures), Prof. Brinkschulte (middleware), Prof. Henkel (low power), Prof. Karl (monitoring), Prof. Wörn (robotics), and Prof. Brändle (neurophysiologic concepts).