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http://dx.doi.org/10.17661/jkiiect.2020.13.6.470

Design and Development of Modular Replaceable AI Server for Image Deep Learning in Social Robots on Edge Devices  

Kang, A-Reum (Electronic Engineering, Kookmin University)
Oh, Hyun-Jeong (Electronic Engineering, Kookmin University)
Kim, Do-Yun (Electronic Engineering, Kookmin University)
Jeong, Gu-Min (Electronic Engineering, Kookmin University)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.13, no.6, 2020 , pp. 470-476 More about this Journal
Abstract
In this paper, we present the design of modular replaceable AI server for image deep learning that separates the server from the Edge Device so as to drive the AI block and the method of data transmission and reception. The modular replaceable AI server for image deep learning can reduce the dependency between social robots and edge devices where the robot's platform will be operated to improve drive stability. When a user requests a function from an AI server for interaction with a social robot, modular functions can be used to return only the results. Modular functions in AI servers can be easily maintained and changed by each module by the server manager. Compared to existing server systems, modular replaceable AI servers produce more efficient performance in terms of server maintenance and scale differences in the programs performed. Through this, more diverse image deep learning can be included in robot scenarios that allow human-robot interaction, and more efficient performance can be achieved when applied to AI servers for image deep learning in addition to robot platforms.
Keywords
AI server; Artificial Intelligence Server; Image Processing; Robot Interaction; Robot Platform;
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