Linking run-time resource management of embedded multi-core platforms with automated design-time exploration

Abstract

Nowadays, owing to unpredictable changes of the environment and workload variation, optimally running multiple applications in terms of quality, performance and power consumption on embedded multi-core platforms is a huge challenge. A lightweight run-time manager, linked with an automated design-time exploration and incorporated in the host processor of the platform, is required to dynamically and efficiently configure the applications according to the available platform resources (e.g. processing elements, memories, communication bandwidth), for minimising the cost (e.g. power consumption), while satisfying the constraints (e.g. deadlines). This study presents a flow linking a design-time design space explorer, coupled with platform simulators at two abstraction levels, with a fast and lightweight priority-based heuristic integrated in the run-time manager to select near-optimal application configurations. To illustrate its feasibility and the very low complexity of the run-time selection, the proposed flow is used to manage the processors and clock frequencies of a multiple-stream MPEG4 encoder chip dedicated to automotive cognitive safety applications

Timing Predictability in Future Multi-Core Avionics Systems

“…Although a lot of research has been performed on multi-processor systems (e.g., [62,87,99]), focus for the great majority of these works is throughput and performance, not worst-case guarantees and predictability, thereby works with a focus on performance boost are not further discussed here.…”

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

“…For example, Mariani et al [17] proposed a run-time management framework in which Pareto-fronts with system configuration points for different applications are determined during design-time DSE, after which heuristics are used to dynamically select a proper system configuration at run time. In [45], a fast and light-weight priority based heuristic is used to select near-optimal configurations explored at design time for the active applications according to the available platform resources. Reference [37] proposes DSE strategies that perform exploration in view of optimizing throughput and energy consumption by considering a generic platform.…”

“…This framework takes advantages of the state-of-the-art solutions for modern adaptive heterogeneous MPSoC systems where the system adaptivity is achieved by adaptively adjusting the mapping of applications to the underlying hardware resources which is optimised at design time. More specifically, we reconsider the method in the state-of-the-art solutions that allows for dynamically reconfiguring the system at run time based on pre-optimized system configurations, such as task mappings derived at design time [17,25,27,29,31,37,45], and extend it by solving the issues of scalability as introduced in [25] and blind adaptivity (a system reconfiguration that should not have happened because of its large overhead actually happened) that are usually existed in these solutions for our target large-scale heterogeneous MPSoC system.…”

An iterative multi-application mapping algorithm for heterogeneous MPSoCs

“…Considering the hybrid task mapping approaches, Mariani et al [15] proposed a run-time management framework in which Pareto-fronts with system configuration points for different applications are determined during design-time DSE, after which heuristics are used to dynamically select a proper system configuration at run time. In [28], a fast and light-weight priority based heuristic is used to select near-optimal configurations explored at design time for the active applications according to the available platform resources. [23] proposes DSE strategies that perform exploration in view of optimizing throughput and energy consumption by considering a generic platform.…”