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Recent progress in hardware and methodology for training neural networks hasushered in a new generation of large networks trained on abundant data. Thesemodels have obtained notable gains in accuracy across many NLP tasks. However,these accuracy improvements depend on the availability of exceptionally largecomputational resources that necessitate similarly substantial energyconsumption. As a result these models are costly to train and develop, bothfinancially, due to the cost of hardware and electricity or cloud compute time,and environmentally, due to the carbon footprint required to fuel modern tensorprocessing hardware. In this paper we bring this issue to the attention of NLPresearchers by quantifying the approximate financial and environmental costs oftraining a variety of recently successful neural network models for NLP. Basedon these findings, we propose actionable recommendations to reduce costs andimprove equity in NLP research and practice.

Energy and Policy Considerations for Deep Learning in NLP