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  11. Prance: Joint Token-optimization And Structural Channel-pruning For Adaptive Vit Inference

PRANCE: Joint Token-Optimization and Structural Channel-Pruning for Adaptive ViT Inference

Ye Li, Chen Tang, Yuan Meng

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 2, 2025

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    View abstract on PubMed

    Summary
    This summary is machine-generated.

    PRANCE accelerates Vision Transformers (ViTs) by jointly optimizing channels and tokens per sample. This framework reduces computational complexity and model size without sacrificing accuracy, enabling efficient ViT deployment.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Vision Transformers (ViTs) face deployment challenges due to large model size and quadratic complexity with token count.
    • Existing methods for accelerating ViTs, like pruning and token reduction, often use fixed ratios and neglect joint optimization, leading to accuracy loss.

    Purpose of the Study:

    • To introduce PRANCE, a novel framework for jointly optimizing activated channels and tokens on a per-sample basis to accelerate ViT inference.
    • To address the challenges of dynamic channel computation and the vast decision space in joint optimization.

    Main Methods:

    • Developed a meta-network with weight-sharing for dynamic channel support in Multi-Head Self-Attention (MHSA) and Multi-Layer Perceptron (MLP) layers.
    • Employed Proximal Policy Optimization (PPO) via a lightweight selector to efficiently manage the combinatorial optimization problem.

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  • Introduced a 'Result-to-Go' training mechanism modeling ViT inference as a Markov decision process to reduce action space and reward delay.

    • Main Results:

    • Achieved approximately 50% reduction in FLOPs (Floating Point Operations).
    • Retained only about 10% of input tokens.
    • Maintained lossless Top-1 accuracy, demonstrating significant efficiency gains.

    Conclusions:

    • PRANCE offers a unified approach to accelerate ViTs by simultaneously optimizing architecture and data.
    • The framework effectively tackles the trade-off between compression and accuracy, enabling efficient deployment of ViTs.