Energy-Efficient Development of ML-Enabled Systems: A Data-Centric Approach
Author:
Affiliation:
1. DISIM, University of L'Aquila, L'Aquila, L'Aquila, Italy
Funder
aquila
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3644815.3644974
Reference34 articles.
1. J. Bornholt T. Mytkowicz and K. S. McKinley. 2012. The model is not enough: Understanding energy consumption in mobile devices. Hot Chips 1--3
2. A. Canziani Adam Paszke and E. Culurciello. 2016. An Analysis of Deep Neural Network Models for Practical Applications. ArXiv abs/1605.07678 (2016).
3. A comprehensive study on challenges in deploying deep learning based software
4. Cuijiao Fu Depei Qian and Zhongzhi Luan. 2018. Estimating Software Energy Consumption with Machine Learning Approach by Software Performance Feature. In 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber Physical and Social Computing (CPSCom) and IEEE Smart Data. (SmartData). 490--496. https:/doi.org10.1109Cybermatics_2018.2018.00106
5. Estimation of energy consumption in machine learning
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