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Latency-Aware Unified Dynamic Networks for Efficient Image Recognition.

Yizeng Han, Zeyu Liu, Zhihang Yuan

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 25, 2024
    PubMed
    Summary

    Latency-Aware Unified Dynamic Networks (LAUDNet) improve deep learning efficiency by unifying dynamic inference paradigms and optimizing hardware scheduling. This framework significantly reduces practical latency while maintaining accuracy across various vision tasks.

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    Area of Science:

    • Deep Learning
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Dynamic networks selectively activate computation units or allocate resources to information-rich regions, reducing unnecessary computations.
    • Practical efficiency of dynamic models often lags behind theoretical potential due to fragmented frameworks, inadequate scheduling strategies, and complex latency evaluation.

    Purpose of the Study:

    • To introduce Latency-Aware Unified Dynamic Networks (LAUDNet), a general framework addressing the practical efficiency gap in dynamic deep learning models.
    • To integrate spatially-adaptive computation, layer skipping, and channel skipping into a unified formulation.
    • To enhance scheduling optimization through a hardware-aware latency predictor.

    Main Methods:

    • Developed LAUDNet, a unified framework integrating three dynamic inference paradigms: spatially-adaptive computation, layer skipping, and channel skipping.

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  • Incorporated a latency predictor to optimize scheduling strategies and accurately estimate inference latency on specific hardware.
  • Evaluated LAUDNet across image classification, object detection, and instance segmentation tasks.

    • Main Results:

    • LAUDNet significantly bridges the gap between theoretical and practical efficiency for dynamic networks.
    • Achieved over 50% practical latency reduction for ResNet-101 on hardware platforms like V100, RTX 3090, and TX2 GPUs.
    • Demonstrated a superior accuracy-efficiency trade-off compared to existing methods.

    Conclusions:

    • LAUDNet provides a unified and efficient solution for dynamic network inference, improving hardware utilization.
    • The framework effectively reduces latency and enhances performance across diverse computer vision applications.
    • LAUDNet represents a significant advancement in optimizing the practical deployment of dynamic deep learning models.