Search Results for author: Xiaolong Tu

Found 2 papers, 0 papers with code

DeepEn2023: Energy Datasets for Edge Artificial Intelligence

no code implementations30 Nov 2023 Xiaolong Tu, Anik Mallik, Haoxin Wang, Jiang Xie

We anticipate that DeepEn2023 will improve transparency in sustainability in on-device deep learning across a range of edge AI systems and applications.

Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices

no code implementations19 Oct 2023 Xiaolong Tu, Anik Mallik, Dawei Chen, Kyungtae Han, Onur Altintas, Haoxin Wang, Jiang Xie

In this paper, we conduct a threefold study, including energy measurement, prediction, and efficiency scoring, with an objective to foster transparency in power and energy consumption within deep learning across various edge devices.

Edge-computing

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