Over the past decade, deep learning models have exhibited considerable
advancements, reaching or even exceeding human-level performance in a range of
visual perception tasks. This remarkable progress has sparked interest in
applying deep networks to real-world applications, such as autonomous vehicles,
mobile devices, robotics, and edge computing. However, the challenge remains
that state-of-the-art models usually demand significant computational
resources, leading to impractical power consumption, latency, or carbon
emissions in real-world scenarios. This trade-off between effectiveness and
efficiency has catalyzed the emergence of a new research focus: computationally
efficient deep learning, which strives to achieve satisfactory performance
while minimizing the computational cost during inference. This review offers an
extensive analysis of this rapidly evolving field by examining four key areas:
1) the development of static or dynamic light-weighted backbone models for the
efficient extraction of discriminative deep representations; 2) the specialized
network architectures or algorithms tailored for specific computer vision
tasks; 3) the techniques employed for compressing deep learning models; and 4)
the strategies for deploying efficient deep networks on hardware platforms.
Additionally, we provide a systematic discussion on the critical challenges
faced in this domain, such as network architecture design, training schemes,
practical efficiency, and more realistic model compression approaches, as well
as potential future research directions.

过去十年来，深度学习模型在各种视觉感知任务中取得了显著进展，但其高计算资源需求限制了其在实际应用中的可行性。本综述通过四个关键领域的研究，即轻量级骨干模型的发展、面向特定计算机视觉任务的专用网络架构或算法、深度学习模型压缩技术以及在硬件平台上部署高效深度网络的策略，对计算效率高的深度学习进行了广泛分析，并讨论了该领域面临的关键挑战和未来研究方向。