RAPID: Layer-Wise Redundancy-Aware Pruning and Importance-Driven Token Merging for Efficient ViT
arXiv:2606.08156v1 Announce Type: cross Abstract: Vision Transformers (ViTs) achieve strong performance but suffer from high computational costs due to quadratic self-attention complexity. Although token reduction techniques such as pruning and merging mitigate this, they typically overlook how representations evolve across network depth. We propose RAPID, a depth-aware token reduction framework that adapts reduction strategies to the layer-wise characteristics of token representations. The primary methodological contribution is a bifurcated strategy: in shallow-to-middle layers, RAPID employs
The continuous growth in complexity and scale of AI models (like ViTs) necessitates ongoing research into efficiency to make them practical for broader applications, especially given current compute and energy constraints.
Efficient AI models can dramatically reduce the computational cost and energy footprint of advanced perception systems, making high-performance AI more accessible and sustainable for deployment across various industries.
This research introduces a novel, depth-aware token reduction method for Vision Transformers which can lead to significant improvements in efficiency without sacrificing performance, potentially accelerating AI development and adoption.
- · AI compute providers (more efficient use of hardware)
- · AI developers
- · Companies deploying vision AI at scale
- · Edge AI device manufacturers
- · High-latency, high-power cloud AI solutions (relatively)
More sophisticated Vision AI models become viable for real-time applications and embedded systems due to reduced computational demands.
The cost of developing and operating advanced AI systems decreases, accelerating AI adoption in sectors with limited compute resources or strict energy budgets.
Increased accessibility of efficient, high-performance AI could democratize advanced computer vision, fostering innovation in new applications beyond current economic or technical limits.
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Read at arXiv cs.AI