The emergence of battery-powered devices has led to an increase of interest in the energy consumption of computing devices. For embedded systems, dispatching the workload on different computing units enables the optimisation of the overall energy consumption on high-performance heterogeneous platforms. However, to use the full power of heterogeneity, architecture specific binary blocks are required, each with different energy/time trade-offs. Finding a scheduling strategy that minimises the energy consumption, while guaranteeing timing constraints creates new challenges. These challenges can only be met by using the full heterogeneous capacity of the platform (e.g. heterogeneous CPU, GPU, DVFS, dynamic frequency changes from within an application).We propose an off-line scheduling algorithm for dependent multiversion tasks based on Forward List Scheduling to minimise the overall energy consumption. Our heuristic accounts for Dynamic Voltage and Frequency Scaling (DVFS) and enables applications to dynamically adapt voltage and frequency during run time. We demonstrate the benefits of multi-version task models coupled with an energy-aware scheduler. We observe that selecting the most energy efficient version for each task does not lead to the lowest energy consumption for the whole application. Then we show that our approach produces schedules that are on average 45.6% more energy efficient than schedules produced by a state-of-the-art scheduling algorithm. Next we compare our heuristic against an optimal solution derived by an Integer Linear Programming (ILP) formulation (deviation of 1.6% on average). Lastly, we empirically show that the energy consumption predicted by our scheduler is close to the actual measured energy consumption on a Odroid-XU4 board (at most -15.8%).
Computational energy-efficiency is a critical aspect of many modern embedded devices as it impacts the level of autonomy for numerous scenarios. We present a component-based energy modeling approach to abstract per-component energy in a dataflow computational network executed according to a given scheduling policy. The approach is based on a modeling tool and ultimately relies on battery state to support a wider range of energy-optimization strategies for power-critical devices. CCS Concepts• Hardware → Power estimation and optimization; • Computing methodologies → Modeling and simulation; • Computer systems organization → Embedded systems; Multicore architectures.
Coordination is a well established computing paradigm with a plethora of languages, abstractions and approaches. Yet, we are not aware of any adoption of the principle of coordination in the broad domain of cyber-physical systems, where non-functional properties, such as execution/response time, energy consumption and security are as crucial as functional correctness.We propose a coordination approach, including a functional coordination language and its associated tool flow, that considers time, energy and security as first-class citizens in application design and development. We primarily target cyber-physical systems running on off-the-shelf heterogeneous multi-core platforms. We illustrate our approach by means of a real-world use case, an unmanned aerial vehicle for autonomous reconnaissance mission, which we develop in close collaboration with industry.
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