Paper Abstract and Keywords |
Presentation |
2023-05-19 09:45
Experimental evaluation of Split computing using actual device Kojin Yorita, Shoki Ohta, Kota Maejima, Kanare Kodera, Yutaro Horikawa, Takayuki Nishio (Tokyo Tech), Kozo Fukui (AceCode) SeMI2023-12 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Machine learning (ML) inference using Deep Neural Network (DNN) with a technique called Split Computing (SC) has attracted attention. SC is a technology for realizing ML applications by coordinating devices with resource-constrained IoT devices and servers. Conventional ML applications have used on-device types, in which inference processing is performed on the device where data is acquired, and cloud/edge types, in which data is sent from the device to the cloud/edge server for processing. However, in on-device types, significant processing delays occur when the device’s computing power is low, and in cloud/edge types, there is a risk of personal information leakage because raw observation data is sent. With SC, processing delays and risks of personal information leakage can be reduced by collaboratively performing inference processing using DNN between devices and edge servers. We implemented SC on a virtual terminal using Docker containers and conducted experimental evaluations using network emulation with the TC command. Specifically, we defined three types of nodes: a cloud server that manages learning models and inference instructions, an edge server that executes distributed inference processing, and IoT devices. We also implemented a function to compress SC and transmitted data at any split point. The experiments demonstrated that changing the split point when the packet loss rate is high can affect the latency and accuracy. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Split computing / Machine learning / Wireless LAN / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 123, no. 31, SeMI2023-12, pp. 50-52, May 2023. |
Paper # |
SeMI2023-12 |
Date of Issue |
2023-05-11 (SeMI) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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SeMI2023-12 |
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