The next generation of embedded software has high performance requirements and is increasingly dynamic. Multiple applications are typically sharing the system, running in parallel in different combinations, starting and stopping their individual execution at different moments in time. The different combinations of applications are forming system execution scenarios. In this paper, we present the distributed application layer, a scenario-based design flow for mapping a set of applications onto heterogeneous on-chip many-core systems. Applications are specified as Kahn process networks and the execution scenarios are combined into a finite state machine. Transitions between scenarios are triggered by behavioral events generated by either running applications or the run-time system. A set of optimal mappings are precalculated during design-time analysis. Later, at runtime, hierarchically organized controllers monitor behavioral events, and apply the precalculated mappings when starting new applications. To handle architectural failures, spare cores are allocated at design-time. At run-time, the controllers have the ability to move all processes assigned to a faulty physical core to a spare core. Finally, we apply the proposed design flow to design and optimize a picture-inpicture software.
This paper presents a static mapping optimization technique for fault-tolerant mixed-criticality MPSoCs. The uncertainties imposed by system hardening and mixed criticality algorithms, such as dynamic task dropping, make the worst-case response time analysis difficult for such systems. We tackle this challenge and propose a worst-case analysis framework that considers both reliability and mixed-criticality concerns. On top of that, we build up a design space exploration engine that optimizes fault-tolerant mixed-criticality MPSoCs and provides worst-case guarantees. We study the mapping optimization considering judicious task dropping, that may impose a certain service degradation. Extensive experiments with real-life and synthetic benchmarks confirm the effectiveness of the proposed technique.
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