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Computer Science > Machine Learning

arXiv:2106.08962 (cs)
[Submitted on 16 Jun 2021]

Title:Efficient Deep Learning: A Survey on Making Deep Learning
Models Smaller, Faster, and Better

Authors:Gaurav Menghani
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    Abstract: 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.

Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2106.08962 [cs.LG]
          (or arXiv:2106.08962v1 [cs.LG] for this version)

Submission history

From: Gaurav Menghani [view email]
[v1] Wed, 16 Jun 2021 17:31:38 UTC (6,125 KB)
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