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Found 12 papers, 6 papers with code
The Two Faces of AI in Green Mobile Computing: A Literature Review
In this paper, we present a review of the literature of the past decade on the usage of artificial intelligence within the realm of green mobile computing.
Batching for Green AI -- An Exploratory Study on Inference
In this study, we examine the effect of input batching on the energy consumption and response times of five fully-trained neural networks for computer vision that were considered state-of-the-art at the time of their publication.
Do DL models and training environments have an impact on energy consumption?
This study aims to analyze the impact of the model architecture and training environment when training greener computer vision models.
Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI
To expand the application of Green AI, we advocate for a shift in the design of deep learning models, by considering the trade-off between energy efficiency and accuracy.
A Systematic Review of Green AI
As a conclusion, the Green AI research field results to have reached a considerable level of maturity.
MLSmellHound: A Context-Aware Code Analysis Tool
In our approach we attempt to resolve this problem by exploring the use of context which includes i) purpose of the source code, ii) technical domain, iii) problem domain, iv) team norms, v) operational environment, and vi) development lifecycle stage to provide contextualised error reporting for code analysis.
Data-Centric Green AI: An Exploratory Empirical Study
Our results show evidence that, by exclusively conducting modifications on datasets, energy consumption can be drastically reduced (up to 92. 16%), often at the cost of a negligible or even absent accuracy decline.
Code Smells for Machine Learning Applications
Hence ensuring code quality is quintessential to avoid issues in the long run.
"Project smells" -- Experiences in Analysing the Software Quality of ML Projects with mllint
We also investigate the perceived importance of these project smells for proof-of-concept versus production-ready ML projects, as well as the perceived obstructions and benefits to using static analysis tools such as mllint.
Systematic Mapping Study on the Machine Learning Lifecycle
However, the advent of AI is bringing an increasing set of practical problems related to AI model lifecycle management that need to be investigated.
The Prevalence of Code Smells in Machine Learning projects
Manual analysis of these smells mainly showed that code duplication is widespread and that the PEP8 convention for identifier naming style may not always be applicable to ML code due to its resemblance with mathematical notation.
AI Lifecycle Models Need To Be Revised. An Exploratory Study in Fintech
Thus, the same development processes and standards in software engineering ought to be complied in artificial intelligence systems.
Software Engineering 68T01 I.2.0; D.2.9
