Sebastian Tobuschat

Predictable and Runtime-Adaptable Network-On-Chip for Mixed-critical Real-time Systems Sebastian Tobuschat

The industry of safety-critical and dependable embedded systems calls for even cheaper, high performance platforms that allow flexibility and an efficient verification of safety and real-time requirements. In this sense, flexibility denotes the ability to (online) adapt a system to changes (e.g. changing environment, application dynamics, errors) and the reuse-ability for different use cases. To cope with the increasing complexity of interconnected functions and to reduce the cost and power consumption of the system, multicore systems are used to efficiently integrate different processing units in the same chip. Networks-on-chip (NoCs), as a modular interconnect, are used as a promising solution for such multiprocessor systems on chip (MPSoCs), due to their scalability and performance. Hence, future NoC designs must face the aforementioned challenges. For safety-critical systems, a major goal is the avoidance of hazards. For this, safety-critical systems are qualified or even certified to prove the correctness of the functioning under all possible cases. A predictable behavior of the NoC can help to ease the qualification process (e.g. formal analysis) of the system. To achieve the required predictability, designers have two classes of solutions: isolation (quality of service (QoS) mechanisms) and (formal) analysis. For mixed-criticality systems, isolation and analysis approaches must be combined to efficiently achieve the desired predictability. Isolation techniques are used to bound interference between different application classes. And analysis can then be applied verifying the real-time applications and sufficient isolation properties. Traditional NoC analysis and architecture concepts tackle only a subpart of the challenges-they focus on either performance or predictability. Existing, predictable NoCs are deemed too expensive and inflexible to host a variety of applications with opposing constraints. And state-of-the-art analyses neglect certain platform pro