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Parallel Optimization of Deep Learning Computation Offloading in Edge Computing Environment
Kwang Yong Shin, Soo-Mook Moon
http://doi.org/10.5626/JOK.2022.49.3.256
Computation offloading to edge servers has been proposed as a solution to performing computation-intensive deep learning applications on devices with low hardware capabilities. However, the deep learning model has to be uploaded to the edge server before computation offloading is possible, a non-trivial assumption in the edge server environment. Incremental offloading of neural networks was proposed as a solution as it can simultaneously upload model and offload computation [1]. Although it reduced the model upload time required for computation offloading, it did not properly handle the model creation overhead, increasing the time required to upload the entire model. This work solves this problem by parallel optimization of model uploading and creation, decreasing the model upload time by up to 30% compared to the previous system.
Design of a Low-power Rendering Interface for Mobile Augmented Reality Headsets
Jaewon Choi, Hyeonjung Park, JeongGil Ko
http://doi.org/10.5626/JOK.2019.46.8.834
Augmented Reality (AR) headsets such as Microsoft HoloLens are demonstrated as the subsequent emergence of AR applications. GPU is one of the major power consumers in the mobile devices according to prior research works. Especially in AR headset environment, the demand for GPU power is expected to be of more importance because of its always-on nature with 3D graphics rendering burden. In this work, we present Low-power Rendering Interface for mobile augmented reality headsets. The proposed system collects all the graphics call s issued from the application layer and optimizes and applies the graphics calls with respect to user quality preservation by considering user head direction information. All the procedures proceed for 90 us and our system reduces ～27% of power consumption in the best cases.
A Prediction-based Dynamic Component Offloading Framework for Mobile Cloud Computing
http://doi.org/10.5626/JOK.2018.45.2.141
Nowadays, mobile computing has become a common computing paradigm that provides convenience to people’s daily life. More and more useful mobile applications’ appearance makes it possible for a user to manage personal schedule, enjoy entertainment, and do many useful activities. However, there are some inherent defects in a mobile device that battery constraints and bandwidth limitations. These drawbacks get a user into troubles when to run computationally intensive applications. As a remedy scheme, component offloading makes room for handling mentioned issues via migrating computationally intensive component to the cloud server. In this paper, we will present the predictive offloading method for efficient mobile cloud computing. At last, we will present experiment result for validating applicability and practicability of our proposal.
Dynamic Power Management for Webpage Loading on Mobile Devices
As the performance of mobile devices has increased, high-end multicore CPUs have become the norm in smartphones. However, such high performance devices are exposed to the problem of battery depletion due to the energy consumption caused by software performance, and despite increases in battery capacity. The required resources are dynamic and varied, and further user interaction is highly random; thus, Linux-based power management such as DVFS is needed to fulfill requirements. In order to reduce power consumption, we propose a method to restrict the CPU frequency of data download while maintaining user reactivity. This can supplement the weakness of existing Linux-based power management techniques like DVFS in relation to webpage loading. Through the implementation of our method at the application level, we confirm that energy consumption from webpage loading is reduced.
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