  Deep Learning has revolutionized the fields of computer vision, natural
language understanding, speech recognition, information retrieval and more.
However, with the progressive improvements in deep learning models, their
number of parameters, latency, resources required to train, etc. have all have
increased significantly. Consequently, it has become important to pay attention
to these footprint metrics of a model as well, not just its quality. We present
and motivate the problem of efficiency in deep learning, followed by a thorough
survey of the five core areas of model efficiency (spanning modeling
techniques, infrastructure, and hardware) and the seminal work there. We also
present an experiment-based guide along with code, for practitioners to
optimize their model training and deployment. We believe this is the first
comprehensive survey in the efficient deep learning space that covers the
landscape of model efficiency from modeling techniques to hardware support. Our
hope is that this survey would provide the reader with the mental model and the
necessary understanding of the field to apply generic efficiency techniques to
immediately get significant improvements, and also equip them with ideas for
further research and experimentation to achieve additional gains.
