What should mobile app developers do about machine learning and energy?
- Published
- Accepted
- Subject Areas
- Artificial Intelligence, Software Engineering
- Keywords
- machine learning, mobile, energy consumption, software energy consumption
- Copyright
- © 2016 McIntosh et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. What should mobile app developers do about machine learning and energy? PeerJ Preprints 4:e2431v1 https://doi.org/10.7287/peerj.preprints.2431v1
Abstract
Machine learning is a popular method of learning functions from data to represent and to classify sensor inputs, multimedia, emails, and calendar events. Smartphone applications have been integrating more and more intelligence in the form of machine learning. Machine learning functionality now appears on most smartphones as voice recognition, spell checking, word disambiguation, face recognition, translation, spatial reasoning, and even natural language summarization. Excited app developers who want to use machine learning on mobile devices face one serious constraint that they did not face on desktop computers or cloud virtual machines: the end-user’s mobile device has limited battery life, thus computationally intensive tasks can harm end-user’s phone availability by draining batteries of their stored energy. How can developers use machine learning and respect the limited battery life of mobile devices? Currently there are few guidelines for developers who want to employ machine learning on mobile devices yet are concerned about software energy consumption of their applications. In this paper we combine empirical measurements of many different machine learning algorithms with complexity theory to provide concrete and theoretically grounded recommendations to developers who want to employ machine learning on smartphones.
Author Comment
This article was sent in a shortened form submitted to ICSE 2017 and is currently under review. This article is an extended version of what was submitted to ICSE 2017.