  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.
