Scalable Design Space Exploration via Answer Set Programming

• Applicants: Professor Dr.-Ing. Christian Haubelt; Professor Dr. Torsten Schaub
• Subject Area: Computer Architecture, Embedded and Massively Parallel Systems ; Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: from 2015 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 269264143

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.

• DFG Programme: Research Grants