| Paper Abstract and Keywords |
| Presentation |
2023-10-31 14:00
[Poster Presentation]
Implementation and Evaluation of Split Computing as a Distributed Machine Learning Inference
Kojin Yorita, Shoki Ohta, Kota Maejima, Kanare Kodera, Yutaro Horikawa, Takayuki Nishio (Tokyo Tech), Kozo Fukui (AceCode) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
Split Computing (SC) is an advanced technology designed to facilitate sophisticated machine learning inference using deep neural networks (DNNs) on resource-constrained devices, particularly IoT devices. Traditional machine learning application approaches primarily involved on-device inference, where data is processed on the device that captures it, or cloud/edge-based inference, where data is transmitted from the device to a cloud/edge server for processing. However, on-device approaches can lead to processing delays if the device has limited computational power, while cloud/edge-based approaches pose risks of personal data leakage due to the transmission of raw observational data. The advantage of SC lies in its ability to effectively mitigate these issues by enabling collaborative DNN inference between the device and the edge server. We implemented this SC technology on actual devices and evaluated its performance through real-world communication. We defined three types of nodes: a cloud server that manages the learning model and provides inference instructions, an edge server that executes distributed inference processing, and an IoT device. We then implemented and evaluated SC at various split points and data compression functionalities. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Split computing / Machine learning / Wireless LAN / / / / / |
| Reference Info. |
IEICE Tech. Rep. |
| Paper # |
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| Conference Information |
| Committee |
RISING |
| Conference Date |
2023-10-30 - 2023-10-31 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Kaderu 2・7 (Sapporo) |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
Cross-Field Research Association of Super-Intelligent Networking |
| Paper Information |
| Registration To |
RISING |
| Conference Code |
2023-10-RISING |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
Implementation and Evaluation of Split Computing as a Distributed Machine Learning Inference |
| Sub Title (in English) |
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| Keyword(1) |
Split computing |
| Keyword(2) |
Machine learning |
| Keyword(3) |
Wireless LAN |
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| 1st Author's Name |
Kojin Yorita |
| 1st Author's Affiliation |
Tokyo Institute of Technology (Tokyo Tech) |
| 2nd Author's Name |
Shoki Ohta |
| 2nd Author's Affiliation |
Tokyo Institute of Technology (Tokyo Tech) |
| 3rd Author's Name |
Kota Maejima |
| 3rd Author's Affiliation |
Tokyo Institute of Technology (Tokyo Tech) |
| 4th Author's Name |
Kanare Kodera |
| 4th Author's Affiliation |
Tokyo Institute of Technology (Tokyo Tech) |
| 5th Author's Name |
Yutaro Horikawa |
| 5th Author's Affiliation |
Tokyo Institute of Technology (Tokyo Tech) |
| 6th Author's Name |
Takayuki Nishio |
| 6th Author's Affiliation |
Tokyo Institute of Technology (Tokyo Tech) |
| 7th Author's Name |
Kozo Fukui |
| 7th Author's Affiliation |
AceCode (AceCode) |
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| Speaker |
Author-1 |
| Date Time |
2023-10-31 14:00:00 |
| Presentation Time |
45 minutes |
| Registration for |
RISING |
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| Volume (vol) |
vol. |
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