Efficiency and productivity for decision making on low-power heterogeneous CPU+GPU SoCs

Markov decision processes provide a formal framework for a computer to make decisions autonomously and intelligently when the effects of its actions are not deterministic. This formalism has had tremendous success in many disciplines; however, its implementation on platforms with scarce computing capabilities and power, as it happens in robotics or autonomous driving, is still limited. To solve this computationally complex problem efficiently under these constraints, high-performance accelerator hardware and parallelized software come to the rescue. In particular, in this work, we evaluate off-line-tuned static and dynamic versus adaptive heterogeneous scheduling strategies for executing value iteration—a core procedure in many decision-making methods, such as reinforcement learning and task planning—on a low-power heterogeneous CPU+GPU SoC that only uses 10–15 W. Our experimental results show that by using CPU+GPU heterogeneous strategies, the computation time and energy required are considerably reduced. They can be up to 54