Scalable Design Space Exploration via Answer Set Programming

该研究项目的目标是在探索速度和适用性方面可持续地提高嵌入式和网络物理系统的自动设计空间探索。设计空间探索的任务是为这些系统的实施确定不同的最佳解决方案。为此，设计和研究了基于答案集编程的嵌入式系统综合与优化方法。在探索过程中，将考虑复杂的静态和动态设计决策，从而扩展适用范围。由此产生的设计空间大大增加，这往往会阻碍最先进的方法进行有效的探索。研究的探索方法的有效性增加，直接将非单调约束检查，这往往源于延迟，吞吐量和功耗要求，在嵌入式系统设计。我们研究（1）新的设计空间探索方法如何从答案集编程的新发展中获益，以及（2）答案集编程领域的新方法，这些方法可以实现有效的设计空间探索。后者包括多目标优化的集成和其他理论求解器的结合。 在第二个项目阶段，我们（1）将重点从设计空间探索中的选择性方法转移到生成系统级方法，即不是从应用程序和平台模板的固定规范开始，而是在设计空间探索期间从组件库构建针对工作负载场景进行优化的计算平台。为此，我们正在利用答案集编程的多功能解决能力。此外，我们（2）还将基于答案集编程的决策集成到嵌入式计算平台本身中，以支持由嵌入式映射器做出的动态设计决策。最后，我们（3）将嵌入式映射器纳入到新的生成设计空间探索的背景理论中，以允许对设计和运行时决策之间的交互进行协调评估。因此，可以给出对所生成的计算平台的质量特性的保证，包括其在给定工作负载下的动态决策。通过研究基于答案集编程的设计空间探索，不仅在设计自动化领域，而且在答案集编程领域的科学发现。

The goal of this research project is to sustainably improve the automatic design space exploration of embedded and cyber-physical systems with respect to exploration speed and applicability. Design space exploration is the task of identifying different optimal solutions for the implementation of these systems. For this purpose, novel embedded systems synthesis and optimization methods based on answer set programming are designed and studied. The area of applicability will be extended in a way that both, complex static but also dynamic design decisions, are considered during exploration. The resulting design space is enormously increased, which often prevents state-of-the-art methods from effective exploration. The effectiveness of the studied exploration methods is increased by directly incorporating non-monotonic constraint checking, which often stems from latency, throughput, and power consumption requirements in embedded systems design. We study (1) how novel design space exploration methods can profit from new developments in answer set programming as well as (2) novel approaches in the domain of answer set programming, which enable effective design space exploration. The latter includes the integration of multi-objective optimization and the incorporation of other theory solvers. In the second project phase, we (1) shift our focus from selective methods in design space exploration to generative system-level methods, i.e. instead of starting with a fixed specification of applications and platform templates, we construct computing platforms, which are optimized for workload scenarios, from a component library during design space exploration. For this purpose, we are exploiting the versatile solving capabilities of answer set programming. Moreover, we (2) also integrate decision-making based on answer set programming into the embedded computing platform itself in order to support dynamic design decisions that are made by an embedded mapper. Finally, we (3) incorporate the embedded mapper into a background theory of the novel generative design space exploration to allow for a coordinated assessment of the interaction between design and run time decisions. As a consequence, guarantees on the quality characteristics for the generated computing platform including its dynamic decision making under a given workload can be given. By studying design space exploration on the basis of answer set programming, scientific findings are not only expected in the domain of design automation but also in the domain of answer set programming.