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Dynamic computation has emerged as a promising avenue to enhance theinference efficiency of deep networks. It allows selective activation ofcomputational units, leading to a reduction in unnecessary computations foreach input sample. However, the actual efficiency of these dynamic models candeviate from theoretical predictions. This mismatch arises from: 1) the lack ofa unified approach due to fragmented research; 2) the focus on algorithm designover critical scheduling strategies, especially in CUDA-enabled GPU contexts;and 3) challenges in measuring practical latency, given that most librariescater to static operations. Addressing these issues, we unveil theLatency-Aware Unified Dynamic Networks (LAUDNet), a framework that integratesthree primary dynamic paradigms-spatially adaptive computation, dynamic layerskipping, and dynamic channel skipping. To bridge the theoretical and practicalefficiency gap, LAUDNet merges algorithmic design with scheduling optimization,guided by a latency predictor that accurately gauges dynamic operator latency.We've tested LAUDNet across multiple vision tasks, demonstrating its capacityto notably reduce the latency of models like ResNet-101 by over 50% onplatforms such as V100, RTX3090, and TX2 GPUs. Notably, LAUDNet stands out inbalancing accuracy and efficiency. Code is available at:https://www.github.com/LeapLabTHU/LAUDNet.

Latency-aware Unified Dynamic Networks for Efficient Image Recognition