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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
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Committee Date Time Place Paper Title / Authors Abstract Paper #
RISING
(3rd)		 2023-10-31
14:00		 Hokkaido Kaderu 2・7 (Sapporo) [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) 		Split Computing (SC) is an advanced technology designed to facilitate sophisticated machine learning inference using dee... [more]
SeMI, IPSJ-ITS, IPSJ-MBL, IPSJ-DPS 2023-05-19
09:45		 Okinawa Okinawa Institute of Science and Technology (OIST)
(Primary: On-site, Secondary: Online)		 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		 Machine learning (ML) inference using Deep Neural Network (DNN) with a technique called Split Computing (SC) has attract... [more] SeMI2023-12
pp.50-52
SeMI, SeMI
(Joint)		 2023-01-19
14:10		 Tokushima Naruto grand hotel
(Primary: On-site, Secondary: Online)		 [Short Paper] A Study of Joint Control Method of Wireless LAN and Machine Learning Settings for Communication-efficient Split Computing
Kojin Yorita, Takayuki Nishio (Tokyo Tech), Daiki Yoda, Toshihisa Nabetani (Toshiba) SeMI2022-77		 Split computing (SC) enables machine learning (ML) inference with a deep neural network on resource-constrained devices.... [more] SeMI2022-77
pp.28-29
SeMI 2022-01-20
15:10		 Nagano
(Primary: On-site, Secondary: Online)		 [Short Paper] A Study of Joint Control of Machine Learning Model and Wireless LAN Parameters in Split inference by Reinforcement Learning
Kojin Yorita (Tokyo Tech.), Sohei Itahara (Kyoto Univ.), Takayuki Nishio (Tokyo Tech.), Daiki Yoda, Toshihisa Nabetani (Toshiba) SeMI2021-66		 Distributed inference (DI) enables machine learning (ML) inference with a deep neural network on resource-constrained de... [more] SeMI2021-66
pp.51-54
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