Games have been one of the most popular applications on smartphones. In order to meet the increasing computational complexity of mobile games, smartphones are now equipped with heterogeneous CPU multi-core architectures like big.LITTLE as well as high-performance GPUs. However, the integrated CPUs and GPUs drain the battery quickly, which has become a bottleneck for improving user experience. In addition to traditional Dynamic Voltage and Frequency Scaling (DVFS) technique for CPUs and GPUs power reduction, heterogeneous multi-core processors, such as the big.LITTLE architecture, have been designed to offer more opportunity for performance-energy tradeoffs. But current processor governors in smartphones can not exploit these power-saving mechanisms wisely, causing considerable energy waste. In this article, we propose a CPU-GPU governing framework that recognizes the performance demand for different game scenes, and select the most energy-efficient hardware configuration for the corresponding scenes. We implement our framework on an ODROID-XU4 mobile platform, and the experiments show that our framework can achieve 26.7, 16.6, and 10.5 percent power saving on average without compromising user experience when compared to the default governor used in our platform and two governors proposed by other researchers, respectively.

Adversarial images are imperceptible perturbations to mislead deep neural networks (DNNs), which have attracted great attention in recent years. Although several defense strategies achieved encouraging robustness against adversarial samples, most of them still failed to consider the robustness on common corruptions (e.g. noise, blur, and weather/digital effects). To address this problem, we propose a simple yet effective method, named Progressive Diversified Augmentation (PDA), which improves the robustness of DNNs by progressively injecting diverse adversarial noises during training. In other words, DNNs trained with PDA achieve better general robustness against both adversarial attacks and common corruptions than other strategies. In addition, PDA also enjoys the advantages of spending less training time and keeping high standard accuracy on clean examples. Further, we theoretically prove that PDA can control the perturbation bound and guarantee better robustness. Extensive results on CIFAR-10, SVHN, ImageNet, CIFAR-10-C and ImageNet-C have demonstrated that PDA comprehensively outperforms its counterparts on the robustness against adversarial examples and common corruptions as well as clean images. More experiments on the frequency-based perturbations and visualized gradients further prove that PDA achieves general robustness and is more aligned with the human visual system.

Graphics rendering is a compute-intensive work and a major source of energy consumption on battery-driven mobile devices. Unlike the existing works that degrade user experience or reuse rendering results coarsely, we propose ReTriple, a fine-grained scheme to reduce rendering workload by reusing the past rendering results at the UI element level. This fine-grained reuse mechanism can explore more opportunities to reduce the workload of the rendering process and save energy. The experiments tested with popular apps show that ReTriple achieves an average speedup of 2.6x and per-frame energy saving of 32.3% for the rendering process while improving user experience.