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http://dx.doi.org/10.6109/jkiice.2022.26.11.1577

Low-Power Streamable AI Software Runtime Execution based on Collaborative Edge-Cloud Image Processing in Metaverse Applications  

Kang, Myeongjin (School of Electronics Engineering, Kyungpook National University)
Kim, Ho (Daegu Science High School)
Park, Jungwon (Daegu Science High School)
Yang, Seongbeom (Daegu Science High School)
Yun, Junseo (Daegu Science High School)
Park, Daejin (School of Electronics Engineering, Kyungpook National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.11, 2022 , pp. 1577-1585 More about this Journal
Abstract
As the interest in the 4th industrial revolution and metaverse increases, metaverse with multi edge structure is proposed and noted. Metaverse is a structure that can create digital doctor-like system through a large amount of image processing and data transmission in a multi edge system. Since metaverse application requires calculating performance, which can reconstruct 3-D space, edge hardware's insufficient calculating performance has been a problem. To provide streamable AI software in runtime, image processing, and data transmission, which is edge's loads, needs to be lightweight. Also lightweight at the edge leads to power consumption reduction of the entire metaverse application system. In this paper, we propose collaborative edge-cloud image processing with remote image processing method and Region of Interest (ROI) to overcome edge's power performance and build streamable and runtime executable AI software. The proposed structure was implemented using a PC and an embedded board, and the reduction of time, power, and network communications were verified.
Keywords
Metaverse; Streamable execution; Video processing; Low-power; Cloud service;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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