Carbon Intensity-Aware Adaptive Inference of DNNs

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Bibliographic Details
Main Author:	Jung, Jiwan
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
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author	Jung, Jiwan
author_facet	Jung, Jiwan
contents	DNN inference, known for its significant energy consumption and the resulting high carbon footprint, can be made more sustainable by adapting model size and accuracy to the varying carbon intensity throughout the day. Our heuristic algorithm uses larger, high-accuracy models during low-intensity periods and smaller, lower-accuracy ones during high-intensity periods. We also introduce a metric, carbon-emission efficiency, which quantitatively measures the efficacy of adaptive model selection in terms of carbon footprint. The evaluation showed that the proposed approach could improve the carbon emission efficiency in improving the accuracy of vision recognition services by up to 80%.
format	Preprint
id	arxiv_https___arxiv_org_abs_2403_15824
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Carbon Intensity-Aware Adaptive Inference of DNNs Jung, Jiwan Machine Learning Artificial Intelligence DNN inference, known for its significant energy consumption and the resulting high carbon footprint, can be made more sustainable by adapting model size and accuracy to the varying carbon intensity throughout the day. Our heuristic algorithm uses larger, high-accuracy models during low-intensity periods and smaller, lower-accuracy ones during high-intensity periods. We also introduce a metric, carbon-emission efficiency, which quantitatively measures the efficacy of adaptive model selection in terms of carbon footprint. The evaluation showed that the proposed approach could improve the carbon emission efficiency in improving the accuracy of vision recognition services by up to 80%.
title	Carbon Intensity-Aware Adaptive Inference of DNNs
topic	Machine Learning Artificial Intelligence
url	https://arxiv.org/abs/2403.15824

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