.png){kind=icon}
返回
Toward Efficient Transparent Computing for IoT Apps by On-Chip Kernel Offload
.png){kind=icon}
PRE
AI
DOI：10.1109/JIOT.2018.2875050.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Graphics processing units (GPUs) have of late enjoyed increased popularity as a general purpose computing accelerator in multiple application domains like artificial intelligence. However, there has been little exploration into the performance and energy optimization GPUs can deliver for increasingly popular but heavy burden on transparent computing for Internet-of-Things (IoT), such as deep-inference tasks under energy-constrained scenarios. This paper presents FuncShare, an extension of the capability of dynamic functional connectivity among lot to cross-device hand over identified computation-intensive functional codes at runtime. As a kernel-level offloading solution, FuncShare now enables unmodified Android and Linux-based transparent computing applications (apps) to utilize not only application functionalities but also system functionalities across devices, as if they were to utilize them inside the same OpenCL or CUDA libraries. Toward secure network connection, FuncShare also allows performing of permission checks for remote apps in the same method as for local. Experimental results show that FuncShare enables transparent cross-device sharing for power-hungry functionalities and benefits significant performance enhancing than within-device processing even if a large amount of data is transferred.
Keyword:
Dynamic functional connectivity
graphics processing unit (GPU)
Internet-of-Things (IoT)
performance optimization
transparent computing
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
机构
引用论文
Block-Stream as a Service: A More Secure, Nimble, and Dynamically Balanced Cloud Service Model for Ambient Computing.png)
IEEE NETWORK
IEEE NETWORKIF6.3
PREAI
分享收藏分享收藏
查看更多内容