BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset
- National Renewable Energy Laboratory
The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER - Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,527 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (number of trainable parameters), 8 network "shapes", and 14 depths on both CPU and GPU hardware collected using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use, and highlights the impact of cache effects. BUTTER-E is intended to be joined with the BUTTER dataset (see "BUTTER - Empirical Deep Learning Dataset on OEDI" resource below) which characterizes the performance of 483k distinct fully connected neural networks but does not include energy measurements.
- Research Organization:
- DOE Open Energy Data Initiative (OEDI); National Renewable Energy Laboratory
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (SC-31)
- Contributing Organization:
- National Renewable Energy Laboratory
- OSTI ID:
- 2329316
- Report Number(s):
- 5991
- Availability:
- OpenEI.Webmaster@nrel.gov
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
BUTTER
BUTTER-E
benchmark
computational science
deep learning
efficient
empirical deep learning
empirical machine learning
energy
energy consumption
energy efficiency
energy use
green computing
machine learning
model
network structure
neural networks
node-level
power
power consumption
training
training efficiency