Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecycle
It provides an overview for researchers and practitioners concerned with the environmental impact of deep learning, but it is incremental as it synthesizes existing literature without introducing new methods.
The paper addresses the high energy consumption of deep learning by reviewing existing approaches to reduce energy usage across the entire deep learning lifecycle, including IT infrastructure, data, modeling, training, deployment, and evaluation.
Deep Learning has enabled many advances in machine learning applications in the last few years. However, since current Deep Learning algorithms require much energy for computations, there are growing concerns about the associated environmental costs. Energy-efficient Deep Learning has received much attention from researchers and has already made much progress in the last couple of years. This paper aims to gather information about these advances from the literature and show how and at which points along the lifecycle of Deep Learning (IT-Infrastructure, Data, Modeling, Training, Deployment, Evaluation) it is possible to reduce energy consumption.