Digital On-Demand Computing Organism: Stability and Robustness

许多生物系统的一个内在特征是它们的自我修复、自我适应、自我配置等能力，或者说是简短的自我X特征。相比之下，今天的计算系统几乎没有任何这些特征，即使他们承诺提高计算到一个新的水平的适用性。我们提出的有机计算方法与基本的自我X机制紧密相关，例如在人体中。从研究基本的生物学机制开始，我们最终得到一个数字化的、按需计算的有机体，它代表了三个层次：大脑、器官和细胞。该按需特性从而强调其对环境请求/变化以及对由计算有机体本身的动态引起的变化的响应性。本文从脑层次出发，提出了一种具有自X特性的机器人控制器软件体系结构。它与器官层面的有机中间件密切互动，具有使用信使的分散控制回路。在单元级，一种新的自适应和动态可重构的硬件架构能够以有效的方式实现self-x功能。在这两者之间，电源管理系统的架构协调大脑级和单元级，以实现超低功耗系统效率。所有级别都提供了监控技术和架构，作为启用self-x功能的先决条件。我们相信，我们对有机计算的综合方法将代表朝着更适应、更节能、更灵活的未来嵌入式实时系统迈出的第一步。拟议的项目由五个研究小组和一名神经生理学专家组成：Becker教授（硬件架构）、Brinkschulte教授（中间件）、Henkel教授（低功耗）、Karl教授（监测）、Wörn教授（机器人）和Brändle教授（神经生理学概念）。

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).