Energy and Policy Considerations for Deep Learning in NLP
It addresses the problem of high energy and financial costs in NLP research, which impacts researchers and the environment, and is incremental in raising awareness and providing recommendations.
The paper quantifies the financial and environmental costs of training large neural network models in NLP, highlighting that accuracy improvements depend on substantial computational resources and energy consumption, and proposes actionable recommendations to reduce these costs and improve equity.
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.