9 citations · 9 across the 3 of their papers we have counts for
3 papers
A Beginner's Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning
Akshaya Jagannadharao, Nicole Beckage, Sovan Biswas +6
Concerns about the environmental footprint of machine learning are increasing. While studies of energy use and emissions of ML models are a growing subfield, most ML researchers an…
Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations
Charles Edison Tripp, Jordan Perr-Sauer, Jamil Gafur +4
Addressing the so-called ``Red-AI'' trend of rising energy consumption by large-scale neural networks, this study investigates the actual energy consumption, as measured by node-le…
The BUTTER Zone: An Empirical Study of Training Dynamics in Fully Connected Neural Networks
Charles Edison Tripp, Jordan Perr-Sauer, Lucas Hayne +2
We present an empirical dataset surveying the deep learning phenomenon on fully-connected feed-forward multilayer perceptron neural networks. The dataset, which is now freely avail…