Latency-aware Unified Dynamic Networks for Efficient Image Recognition (2308.15949v3)

Abstract: Dynamic computation has emerged as a promising avenue to enhance the inference efficiency of deep networks. It allows selective activation of computational units, leading to a reduction in unnecessary computations for each input sample. However, the actual efficiency of these dynamic models can deviate from theoretical predictions. This mismatch arises from: 1) the lack of a unified approach due to fragmented research; 2) the focus on algorithm design over critical scheduling strategies, especially in CUDA-enabled GPU contexts; and 3) challenges in measuring practical latency, given that most libraries cater to static operations. Addressing these issues, we unveil the Latency-Aware Unified Dynamic Networks (LAUDNet), a framework that integrates three primary dynamic paradigms-spatially adaptive computation, dynamic layer skipping, and dynamic channel skipping. To bridge the theoretical and practical efficiency 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 capacity to notably reduce the latency of models like ResNet-101 by over 50% on platforms such as V100, RTX3090, and TX2 GPUs. Notably, LAUDNet stands out in balancing accuracy and efficiency. Code is available at: https://www.github.com/LeapLabTHU/LAUDNet.

• The paper introduces LAUDNet, a unified framework merging various dynamic inference paradigms to bridge the gap between theoretical computational savings and practical latency improvements in deep networks.
• LAUDNet integrates algorithmic design with scheduling optimization using a latency prediction model that considers hardware specifics, addressing key challenges in dynamic network deployment.
• Empirical results show LAUDNet reduces latency by over 50% on platforms like V100, RTX3090, and TX2 GPUs for ResNet-101, demonstrating superior accuracy-efficiency trade-offs.

Latency-aware Unified Dynamic Networks for Efficient Image Recognition

The paper "Latency-aware Unified Dynamic Networks for Efficient Image Recognition" introduces a novel framework for enhancing the practical efficiency of deep neural networks through dynamic computation. The authors examine the discrepancies between theoretical computational savings and practical latency improvements, proposing Latency-Aware Unified Dynamic Networks (LAUDNet) as a solution. LAUDNet merges various dynamic inference paradigms, including spatially-adaptive computation, dynamic layer skipping, and dynamic channel skipping, within a unified formulation.

Key Challenges and Proposed Solutions

The authors identify three primary challenges hindering the practical deployment of dynamic networks: the lack of a unified framework for dynamic inference, excessive focus on algorithm design over execution scheduling, and difficulty in measuring practical latency. To address these, LAUDNet integrates algorithmic design with scheduling optimization, leveraging a latency prediction model that accounts for the interplay between algorithms, scheduling strategies, and hardware characteristics.

Empirical Evaluation

The experiments validate LAUDNet's efficacy in bridging the gap between theoretical and practical efficiency, demonstrating a substantial reduction in latency compared to static counterparts. For example, LAUDNet achieves over 50% reduction in latency on ResNet-101 across various platforms such as V100, RTX3090, and TX2 GPUs. This demonstrates LAUDNet's superior performance in accuracy-efficiency trade-offs over previous methods.

Advantages of Dynamic Computation

Dynamic models allocate computation adaptively based on the informativeness of regions within the input image, leading to reduced computational redundancy compared to static models. By incorporating dynamic computation paradigms into a unified framework, LAUDNet enhances deployment on resource-constrained platforms where efficient use of computational resources is crucial.

Implications for AI Development

The practical application of dynamic networks facilitated by LAUDNet could lead to more resource-efficient AI systems, particularly in scenarios involving constrained computational resources like mobile devices or edge computing. The ability to accurately predict latency and optimize scheduling may inform future designs of neural architectures that maximize efficiency without sacrificing performance.

Future Directions

Future research could explore extending LAUDNet to more diverse model architectures and tasks, such as vision-language tasks and low-level vision applications. This could potentially broaden the impact of dynamic networks across various domains within AI.

In summary, this paper presents LAUDNet as a comprehensive framework that effectively unifies dynamic computation strategies, improves practical latency, and enhances the deployment efficiency of deep networks. Through rigorous empirical validation, the framework demonstrates significant advancements in bridging the gap between theoretical computational savings and real-world efficiency.