Digital On-Demand Computing Organism: Stability and Robustness

数字按需计算有机体：稳定性和鲁棒性

• 批准号: 5453594
• 负责人: Professor Dr.-Ing. Jürgen Becker
• 金额: --
• 依托单位: Institut für Technik der Informationsverarbeitung (ITIV)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2005
• 资助国家: 德国
• 起止时间: 2004-12-31 至 2013-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/5453594?language=en
• 关键词: Digital; Demand; Computing; Organism; Stability

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).

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，或简短的自我x特征。相比之下，今天的S计算系统几乎没有任何这些特征，尽管它们承诺将计算的适用性提高到一个新的水平。我们提出的有机计算方法与基本的自-x机制紧密相连，例如在人体中发现的机制。从研究基本的生物机制开始，我们最终得出了一个数字的、按需计算的有机体，代表了大脑、器官和细胞三个层次。因此，按需特性强调其对环境请求/变化以及由计算有机体本身的动态引起的变化的响应性。从人脑层面出发，提出了一种强调自x特性的机器人控制器的软件体系结构。它与器官级别的有机中间件紧密交互，具有使用信使的分散控制循环。在单元级，一种新颖的自适应和动态可重构的硬件体系结构能够有效地实现self-x特性。在此期间，电源管理系统？S架构协调大脑和细胞级别，以实现超低电源系统效率。所有级别都提供了监控技术和体系结构，作为启用Self-x功能的先决条件。我们相信，我们对有机计算的全面方法将代表着朝着更适应、更节能和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(低功率)、Karl教授(监控)、Wörn教授(机器人)和Brndle教授(神经生理学概念)。