We have proposed a method to use on-device deep learning inference to detect activities that users are doing as feedback for optimizing activity data collection ...
While mobile and embedded devices are increasingly using deep learning models to infer user context, we propose to exploit on-device deep ...
This work proposes to exploit on-device deep learning inference using a long short-term memory (LSTM)-based method to alleviate the labeling effort and ...
We discuss the results, limitations, challenges, and implications for on-device deep learning inference that support activity data collection. Also, we publish ...
공동 저자 ; On-device deep learning inference for efficient activity data collection. N Mairittha, T Mairittha, S Inoue. Sensors 19 (15), 3434, 2019. 14, 2019.
Oct 12, 2022 · I've heard that ML inference is starting more and more to move to the edge devices instead of being run on the cloud.
In this paper, we propose a two-stage pipeline that optimizes DL models on target devices. The first stage optimizes the inference workloads, and the second ...
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공동 저자 ; On-device deep learning inference for efficient activity data collection. N Mairittha, T Mairittha, S Inoue. Sensors 19 (15), 3434, 2019. 14, 2019.
Dec 23, 2020 · Based on our findings, we highlight critical and promising future research directions regarding the design of efficient activity data collection ...
Deep learning is based upon architectures of artificial neural networks containing many hidden layers. While training such architectures was once impractical, ...