Graphics-intensive mobile games place different and varying levels of demand on the associated central processing units (CPUs) and graphics processing units (GPUs). In contrast to the workload variability that characterizes games, the current design of the energy governor employed by mobile systems appears to be outdated. In this paper, we review the energy-saving mechanism implemented in an Android system coupled with graphics-intensive gaming workloads from three perspectives: 1) user perception; 2) application status; and 3) the interplay between the CPU and GPU. We observe that there are information gaps in the current system, which may result in unnecessary energy wastage. To resolve the problem, we propose an online user-centric CPU-GPU governing framework. To bridge the identified information gaps, we classify rendered game frames into redundant/changing frames to satisfy user demand, categorize an application into GPU sensitive/insensitive phases to understand the application's demand, and determine the frequency scaling intents of the CPU and GPU to capture processor demand. In response to the measured demand, we employ a required workload estimator, a unified policy selector, and a frequency-scaling intent communicator in the framework to save energy. The proposed framework was implemented on an LG Nexus 5X smartphone, and extensive experiments with real-world 3-D gaming applications were conducted. According to the experiment results, for an application which is low interactive and infrequent phase changing, the proposed framework can, respectively, reduce energy consumption by 25.3% and 39% compared with our previous work and Android governors while maintaining user experience.

In a multi-core platform, resources, such as memory banks and buses, are mostly shared among all cores for power, performance, and cost reasons. The access interference on the shared resources poses a major challenge on the analysis of real-time properties, but can be alleviated if task data partition onto memory banks is applied with care. In this paper, we consider to schedule RAS (resource access sporadic) tasks onto a platform consisting of homogeneous cores and capacity-limited memory banks. According to our observation, we should avoid internal data spreading among the memory banks for a task while advocate external data spreading among memory banks for a given task set. We propose a two-phase algorithm with (4 + ρ + 3(2γ+1)/γ) speedup factor and (γ + 1) memory augmentation factor, where ρ γ 0 and ρ ≥ 1. The derived adjustable resource augmentation factors can be useful in terms of system synthesis and schedulability. Moreover, under the premise that a given task set is feasible, we devise a bi-section approach that can derive a schedulable solution requiring the least amount of memory augmentation. According to our experiment results, the proposed algorithm significantly outperformed the state-of-the-art algorithm [15] in terms of schedulability test even when memory augmentation is prohibited.

Application paradigms will increasingly exceed a mobile device's physical boundaries. This paper presents a system solution for a mobile device to mount remote sensors on other devices. Our design is generic to mobile senor stacks, thus supporting unmodified apps and commodity sensors. Furthermore, it uses an asynchronous access model to facilitate semantics passing and data reporting in between. Such semantic information allows the development of an energy-efficient reporting policy for remote sensing applications. The results of experiments conducted on commercial Android smartphones with popular apps demonstrate that our design is very efficient in terms of energy consumption and completion time.

Resident applications, which autonomously awaken mobile devices, can gradually and imperceptibly drain device batteries. This paper introduces the concept of alarm similarity into wakeup management for mobile systems in connected standby. First, we define hardware similarity to reflect the degree of energy savings and time similarity to reflect the impact on user experience. We then propose a policy that aligns alarms based on their similarity to save standby energy while maintaining the quality of the user experience. Finally, we integrate our design into Android and conduct extensive experiments on a commercial smartphone running popular mobile apps. The results demonstrate that our design can further extend the standby time achieved with Android's native policy by up to one-third.

This work is motivated by the demand for scheduling tasks upon the increasingly popular island-based many-core architectures. On such an architecture, homogeneous cores are grouped into islands, each of which is equipped with a scratchpad memory module (referred to as local memory). We first show the NP-hardness and the inapproximability of the scheduling problem. Despite the inapproximability, positive results can still be found when different cases of the problem are investigated. A$(3-\frac{1}{F})$- approximation algorithm is proposed for the minimization of the maximum system utilization, where$F$is the number of cores in the platform. When the technique of resource augmentation is considered, this paper further develops a$(\gamma +1)$-memory$\frac{2\gamma -1}{\gamma -1}$-approximation algorithm, where$\gamma$represents the trade-off between CPU utilization and local memory space. On the other hand, a special case is also considered when the ratio of the worst-case execution time of a task without and with using the local memory is bounded by a constant. The capabilities of the proposed algorithms are then evaluated with benchmarks from MRTC, UTDSP, NetBench and DSPstone, where the maximum system utilization can be significantly reduced even when the local memory size is only 5 percent of the total footprint of all of the tasks.

Recently, phase change memory (PCM) has become a promising candidate to replace dynamic RAM as main memory due to its low power consumption, fast I/O performance, and byte addressability. Accompanied with the merits, the adoption of PCM may suffer from its physical characteristic of limited write endurance. Wear leveling is a well-known approach to address this issue. For PCM main memory, the design of wear leveling should stress operation efficiency and overhead reduction. Nevertheless, conventional designs are usually dedicated to prolonging the lifetime of PCM in the best effort. In this paper, we propose a novel perspective that, instead of valuing PCM lifetime exploitation as the first priority, we turn to satisfy the product warranty period. With such a paradigm shift, the management overhead of wear-leveling mechanisms could be reduced so as to achieve further enhancement of operation efficiency. To this end, we propose a warranty-aware page management design that introduces novel criteria used to determine the state of a page by taking both the product warranty period and the write cycles of a page into consideration. Theoretical analysis is also conducted to investigate the properties and performance of the proposed management. To show the effectiveness of the proposed design, we collected real traces by running SPEC2006 benchmarks with different write intensity workloads. The experimental results showed that our design reduced the overhead to one-third that of the state-of-the-art designs while still providing the same level of performance.

Graphics-intensive mobile games are becoming increasingly popular, but such applications place high demand on device CPUs and GPUs. The design of current mobile systems results in unnecessary energy waste due to lack of consideration of application phases and user attention (a “demand-level” gap) and because each processor administers power management autonomously (a “processor-level” gap). This paper proposes a user-centric CPU-GPU governing framework which aims to reduce energy consumption without significantly impacting the user experience. To bridge the gap at the demand level, we identify the user demand at runtime and accordingly determine appropriate governing policies for the respective processors. On the other hand, to bridge the gap at the processor level, the proposed framework interprets the frequency scaling intents of processors based on the observation of the CPU-GPU interaction and the processor status. We implemented our framework on a Samsung Galaxy S4, and conducted extensive experiments with real-world 3D gaming apps. Experimental results showed that, for an application being highly interactive and frequent phase changing, our proposed framework can reduce energy consumption by 45.1% compared with state-of-the-art policy without significantly impacting the user experience.

An increasing number of mobile devices are being equipped with 802.11n interfaces to support bandwidth-intensive applications; however, the improved bandwidth increases power consumption. To address the issue, researchers are focusing on antenna management. In this paper, we present a dynamic antenna management (DAM) scheme to improve the uplink energy efficiency on mobile devices whose packet workloads may vary significantly and frequently. First, we model antenna management as an optimization problem, with the objective of minimizing the energy required to transmit a sequence of variable-length packets with random arrival times. Then, we propose an optimal offline algorithm to solve the problem, as well as a competitive online algorithm that has a provable performance guarantee and allows compatible implementations on 802.11n mobile devices. To evaluate our scheme, we conducted extensive simulations based on real mobile user traces and application transmission patterns. Nearly all commercial 802.11n mobile devices support the power save mode (PSM). Our results demonstrate that DAM can improve the energy efficiency of PSM significantly at a cost of slight throughput degradation.

The thriving growth in mobile consumer electronics makes energy efficiency in the embedded system design an important and recurring theme. Phase Change Memory (PCM) has shown its potential in replacing DRAM as the main memory option due to its (65%) reduced energy requirements. However, when considering the usage of PCM main memory, its write endurance becomes a critical issue, and wear leveling design is a common approach to resolve this issue. Even though the wear leveling design should stress operation efficiency and overhead reduction, existing wear leveling strategies designed for PCM main memory are usually dedicated to prolonging the lifetime of PCM. In this paper, we propose the perspective that, instead of valuing PCM lifetime exploitation as the first priority, we should turn to satisfy the product warranty period. To this end, further enhancement of operation efficiency and reduction of management overhead could be achieved. We thus propose a warranty-aware page management design to enhance the operation efficiency for managing the endurance issue in PCM. To show the effectiveness of the proposed design, we collected real traces on fiasco. OC by running SPEC2006 benchmarks with different write intensity workloads. The experiment results showed that our design reduced the overhead to one third of that of the state-of-the-art designs while still providing the same level of performance.

This work is motivated by the advance of heterogeneous computing and the strong demands of workload acceleration in practice. By considering pipeline workloads over FPGA, this paper explores a systematic methodology to configure the hardware instances of each pipeline stage such that the maximum of the execution time of each stage is minimized, where the FPGA allocation with the memory bandwidth constraint is considered. For the target problem, an algorithm is proposed and proved being optimal, and a real implementation study is conducted. In the experimental results, an image filter FPGA implementation can outperform the CPU, GPU, and baseline FPGA solutions by 460%, 73%, and 1030%, respectively. Extensive simulations were also conducted with a large FPGA size to show the scalability of this work.