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    Engineering our Future: Experimentierfeld Campus (LS4)
    Um zukünftige Probleme zu lösen, brauchen wir Ingenieur*innen, die die rasanten technologischen Fortschritte und die komplexe Welt aktiv gestalten können. An technischen Hochschulen ist daher ein Innovationsschub nötig, um die heterogene Studierendenschaft auf die interdisziplinären Anforderungen vorzubereiten. Die Technische Universität Hamburg (TUHH) nimmt diese Herausforderung mit ihrem Projekt "Engineering our Future: Experimentierfeld Campus" an. Sie verwandelt ihren Campus in ein lebendiges Experimentierfeld und stellt das praxisorientierte Lernen in den Mittelpunkt der Curricula und Hochschulstrukturen. Unter dem Motto „Engineering to Face Climate Change“ arbeiten Studierende in Projektteams an einer klimafreundlichen Gestaltung des Campus. Durch praktische Erfahrungen lernen die Studierenden konkrete Problemlösung, kritisches Denken und Selbstorganisation. Für Hochschule und Öffentlichkeit wird in einem Campus-Walk ganz praktisch veranschaulicht, welchen wichtigen Beitrag die Ingenieurwissenschaften zur Bewältigung der Klimakrise leisten können. Die Initiative erweitert bestehende innovative Ansätze und Infrastrukturen der Universität. Ein zentraler Aspekt des Projekts ist die Flexibilisierung der Studienstrukturen, etwa durch Blockveranstaltungen in den Curricula, die intensives experimentelles Lernen ermöglichen. Mit ihrem Campuslabor schafft die TUHH eine praxisnahe, interdisziplinäre Lernumgebung, die Lehre mit gesellschaftlichen Zielen verknüpft. Das Institut für Verkehrsplanung und Logistik ist am Projekt ENGINEXT mit dem Leitstudiengang 4 „Wirtschaftsingenieurwesen“, welcher sich in der Entwicklung findet, beteiligt.
      20  5
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    GRK 2972: Concepts and Algorithms for – and Usage of – Self-Explaining Digitally Controlled Systems
    Today, almost any technical system is digitally controlled which adds to its smartness. Such digitally controlled systems feature many and functionally diverse interacting subsystems, system levels, and connections to other systems. Many problems arising during their design and operation can be traced back to a lack of understanding and consequential mismatches at their interfaces. Avoiding such fallacies, demands mutual explanations of behaviour comprehensible to other systems, designers, developers, and operators. Developers cannot be expected to understand all details of all components of a Cyber Physical System (CPS) and the environment. Yet software engineers, e.g., would need comprehensible explanations of expectations and guarantees of a physical process in the environment directly or indirectly interfacing with the program. In an ideal model-based design world, all the engineers would have to know would have been encoded in reasonable design specifications or contracts. Realistically, these are however partial and focused on precision, demanding understandable explanations for specific situations as a complement. The research training group CAUSE addresses these issues by striving to make digitally controlled systems self-explaining to developers, users, and other systems. While the term explanation and its relation to understanding and reasoning have been discussed intensively in many disciplines, we provide a definition that fits our view and needs to be later related to our formalisms: An explanation is information timely provided by one (self-explaining) system (the explainer) to another system (the addressee); the explanation empowers the addressee to unveil consequences from decisions or inconsistencies in knowledge remaining inaccessible otherwise; by means of the explanation, the addressee can draw these conclusions without knowing all details of the interaction. CAUSE focuses on the technical, logical, and algorithmic basis of self-explanation capabilities and their integration into system engineering methodology at all abstraction levels pertinent to digital system design. The research training group is unique in fostering and facilitating development of complementing theories and methods using a demonstrator covering all levels from digital hardware over software and systems to Systems of CPSs. The research training group spans three institutions implementing a consortium equally versed in hardware, software, and systems, as well as in theoretical foundations, development methods, and practical applications. Within CAUSE, doctoral researchers are immersed into state-of-the-art research concerning novel system design concepts enabling and building upon self-explanation. They develop scientific independence and acquire the timely skills of distributed team-work, working in varying collaborative contexts, adapting to diverse knowledge backgrounds, and intense technical communication.
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    Combining Testing and Monitoring for Online Functional Guarantees in Imprecise Hardware Systems
    Two forces are shaping the design of new digital systems: variability and approximation. As technology scales, the variability of devices and interconnects increases dramatically due to intrinsic (e.g., unequal dopant concentrations) and extrinsic (e.g., temperature variations) factors. Furthermore, devices degrade over time -the so-called aging- which exacerbates the variability problem. To palliate these issues, new design methodologies proposed to accept an "imprecise" functionality at some parts of the design and during some periods of time. Thus, errors are becoming an integral part of the design flow. The "imprecise" functionality appears in two fundamentally different flavors: either it is due to the unavoidable variability of the technology which is addressed with additional logic -the so-called stochastic processing- or it is created by design to reduce area or energy consumption of the system -the so-called approximate processing. These two characteristics will play an essential role when considering future systems, e.g., enhancing the infrastructure for the Internet of Things (IoT) and creating multiprocessor systems for high-performance computing.Currently, it is not possible to quantify the accuracy-warranties of an imprecise processing system reliably at run time when it includes imprecise hardware subject to aging and environmental changes. Consequently, safely deploying adaptable systems that use functional approximation and that are implemented in new variability-prone technologies is difficult. Our main goal is to overcome this problem by developing new concepts for the first run-time diagnostic infrastructure for imprecise circuits that provides functional guarantees. We want to achieve this by combining new algorithms for generation and synthesis of stimuli-based testing tightly joined with new hardware building blocks for monitoring functionality of a system at run time. We will validate our design tools and methodology on a Vision-System-on-Chip.
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