BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset

Published by National Renewable Energy Laboratory | Department of Energy | Metadata Last Checked: June 28, 2025 | Last Modified: 2024-10-07T15:12:02Z

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.

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Complete Metadata

bureauCode	[ "019:20" ]
dataQuality	true
DOI	10.25984/2329316
identifier	https://data.openei.org/submissions/5991
issued	2022-12-30T07:00:00Z
landingPage	https://data.openei.org/submissions/5991
programCode	[ "019:023" ]
projectNumber	GO0028308
projectTitle	National Renewable Energy Laboratory (NREL) Lab Directed Research and Development (LDRD)
spatial	{"type":"Polygon","coordinates":[[[-180,-83],[180,-83],[180,83],[-180,83],[-180,-83]]]}

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