A workflow for runtime adaptive task allocation on heterogeneous MPSoCs

Abstract

Modern Multiprocessor Systems-on-Chips (MPSoCs) are ideal platforms for co-hosting multiple applications, which may have very distinct resource requirements (e.g. data processing intensive or communication intensive) and may start/stop execution independently at time instants unknown at design time. In such systems, the runtime task allocator, which is responsible for assigning appropriate resources to each task, is a key component to achieve high system performance. This paper presents a new task allocation strategy in which self-adaptability is introduced. By dynamically adjusting a set of key parameters at runtime, the optimization criteria of the task allocator adapts itself according to the relative scarcity of different types of resources, so that resource bottlenecks can be effectively mitigated. Compared with traditional task allocators with fixed optimization criteria, experimental results show that our adaptive task allocator achieves significant improvement both in terms of hardware efficiency and stability.