9 citations · 9 across the 2 of their papers we have counts for
2 papers
cs.LG2024★ 9 cited
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…
cs.LG2022
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…