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        <title>Energy and Policy Considerations for Deep Learning in NLP</title>
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        <namePart type="given">Emma</namePart>
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    <abstract>Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in accuracy across many NLP tasks. However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption. As a result these models are costly to train and develop, both financially, due to the cost of hardware and electricity or cloud compute time, and environmentally, due to the carbon footprint required to fuel modern tensor processing hardware. In this paper we bring this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training a variety of recently successful neural network models for NLP. Based on these findings, we propose actionable recommendations to reduce costs and improve equity in NLP research and practice.</abstract>
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%0 Conference Proceedings
%T Energy and Policy Considerations for Deep Learning in NLP
%A Strubell, Emma
%A Ganesh, Ananya
%A McCallum, Andrew
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F strubell-etal-2019-energy
%X Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in accuracy across many NLP tasks. However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption. As a result these models are costly to train and develop, both financially, due to the cost of hardware and electricity or cloud compute time, and environmentally, due to the carbon footprint required to fuel modern tensor processing hardware. In this paper we bring this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training a variety of recently successful neural network models for NLP. Based on these findings, we propose actionable recommendations to reduce costs and improve equity in NLP research and practice.
%R 10.18653/v1/P19-1355
%U https://aclanthology.org/P19-1355
%U https://doi.org/10.18653/v1/P19-1355
%P 3645-3650

Markdown (Informal)

[Energy and Policy Considerations for Deep Learning in NLP](https://aclanthology.org/P19-1355) (Strubell et al., ACL 2019)

• Energy and Policy Considerations for Deep Learning in NLP (Strubell et al., ACL 2019)

ACL

• Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019. Energy and Policy Considerations for Deep Learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3645–3650, Florence, Italy. Association for Computational Linguistics.