Performance and Thermal Management on Self-Adaptive Hybrid Multi-cores

Handling run-time dynamics on embedded system-on-chip architectures has become more challenging over the years. On the one hand, the impact of workload and physical dynamics on the system behavior has dramatically increased. On the other hand, embedded architectures have become more complex as they have evolved from single-processor systems over multi-processor systems to hybrid multi-core platforms. Static design-time techniques no longer provide suitable solutions to deal with the...
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Handling run-time dynamics on embedded system-on-chip architectures has become more challenging over the years. On the one hand, the impact of workload and physical dynamics on the system behavior has dramatically increased. On the other hand, embedded architectures have become more complex as they have evolved from single-processor systems over multi-processor systems to hybrid multi-core platforms. Static design-time techniques no longer provide suitable solutions to deal with the run-time dynamics of today's embedded systems. Therefore, system designers have to apply run-time solutions, which have hardly been investigated for hybrid multi-core platforms. In this thesis, we present fundamental work in the new area of run-time management on hybrid multi-core platforms. We propose a novel architecture, a self-adaptive hybrid multi-core system, that combines heterogeneous processors, reconfigurable hardware cores, and monitoring cores on a single chip. Using self-adaptation on thread-level, our hybrid multi-core systems can effectively perform performance and thermal management autonomously at run-time.