Cost-constrained QoS optimization for approximate computation real-time tasks in heterogeneous MPSoCs

Internet of Things devices, such as video-based detectors or road side units are being deployed in emerging applications like sustainable and intelligent transportation systems. Oftentimes, stringent operation and energy cost constraints are exerted on this type of applications, necessitating a hybrid supply of renewable and grid energy. The key issue of a cost-constrained hybrid of renewable and grid power is its uncertainty in energy availability. The characteristic of approximate computation that accepts an approximate result when energy is limited and executes more computations yielding better results if more energy is available, can be exploited to intelligently handle the uncertainty. In this paper, we first propose an energy-adaptive task allocation scheme that optimally assigns real-time approximate-computation tasks to individual processors and subsequently enables a matching of the cost-constrained hybrid supply of energy with the energy demand of the resultant task schedule. We then present a quality of service (QoS)-driven task scheduling scheme that determines the optional execution cycles of tasks on individual processors for optimization of system QoS. A dynamic task scheduling scheme is also designed to adapt at runtime the task execution to the varying amount of the available energy. Simulation results show that our schemes can reduce system energy consumption by up to 29% and improve system QoS by up to 108% as compared to benchmarking algorithms.