A Run-time Self-adaptive Resource Allocation Framework for MPSoC Systems

Self-adaptivity is becoming a key feature of modern embedded systems to meet performance and power constraints in increasingly common situations where embedded application workloads show highly dynamic behavior. This paper presents a scalable framework for adaptive MultiProcessor System-on-Chip (MPSoC) systems that allows for adaptivity throttling

A hierarchical run-time adaptive resource allocation framework for large-scale MPSoC systems

In the embedded computer system domain, MPSoC systems have become increasingly popular due to the ever-increasing performance demands of modern embedded applications. The number of processing elements in these MPSoCs also steadily increases. Whereas current MPSoCs still contain a limited number of processing elements, future MPSoCs will feature tens up to hundreds of (heterogeneous) processing elements that are all integrated on a single chip. On these future large-scale MPSoC systems, the mapping of applications onto the hardware resources plays an important role to fully explore the parallelism of applications. In this article, a hierarchical run-time adaptive resource allocation framework which uses an intelligent task remapping approach is proposed to improve the system performance for large-scale MPSoCs