Summary of Measuring the Energy Consumption and Efficiency Of Deep Neural Networks: An Empirical Analysis and Design Recommendations, by Charles Edison Tripp et al.
Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations
by Charles Edison Tripp, Jordan Perr-Sauer, Jamil Gafur, Amabarish Nag, Avi Purkayastha, Sagi Zisman, Erik A. Bensen
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the actual energy consumption of training various fully connected neural network architectures using node-level watt-meters. The study introduces the BUTTER-E dataset, which contains energy consumption and performance data from 63,527 experimental runs across 30,582 configurations. The analysis reveals a complex relationship between dataset size, network structure, and energy use, highlighting cache effects’ impact. A straightforward energy model is proposed, accounting for network size, computing, and memory hierarchy. The study also uncovers a non-linear relationship between energy efficiency and network design, challenging the assumption that reducing parameters or FLOPs leads to greater energy efficiency. This work contributes to sustainable computing and Green AI, offering practical guidance for creating more energy-efficient neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how much energy it takes to train different types of neural networks using special meters that measure energy use. They created a big dataset with lots of data points from running many experiments on different computers and networks. The study shows that there’s a complicated relationship between the size of the network, the type of computer it’s run on, and how much energy it uses. They also found out that you can’t just make the network smaller to save energy – it’s more complex than that. This research helps us create more energy-efficient neural networks and be kinder to the environment. |
Keywords
* Artificial intelligence * Neural network
