Rethinking Vision Transformer Depth via Structural Reparameterization (2511.19718v1)

Abstract: The computational overhead of Vision Transformers in practice stems fundamentally from their deep architectures, yet existing acceleration strategies have primarily targeted algorithmic-level optimizations such as token pruning and attention speedup. This leaves an underexplored research question: can we reduce the number of stacked transformer layers while maintaining comparable representational capacity? To answer this, we propose a branch-based structural reparameterization technique that operates during the training phase. Our approach leverages parallel branches within transformer blocks that can be systematically consolidated into streamlined single-path models suitable for inference deployment. The consolidation mechanism works by gradually merging branches at the entry points of nonlinear components, enabling both feed-forward networks (FFN) and multi-head self-attention (MHSA) modules to undergo exact mathematical reparameterization without inducing approximation errors at test time. When applied to ViT-Tiny, the framework successfully reduces the original 12-layer architecture to 6, 4, or as few as 3 layers while maintaining classification accuracy on ImageNet-1K. The resulting compressed models achieve inference speedups of up to 37% on mobile CPU platforms. Our findings suggest that the conventional wisdom favoring extremely deep transformer stacks may be unnecessarily restrictive, and point toward new opportunities for constructing efficient vision transformers.

• The paper introduces a novel branch-based reparameterization method that significantly reduces the number of transformer layers without losing accuracy.
• It employs exact branch consolidation in FFN and MHSA modules to merge parallel training paths into a single efficient inference model.
• The approach achieves up to 64% throughput improvements and 37% reduced latency on mobile CPUs, challenging the need for deep architectures in ViTs.

Rethinking Vision Transformer Depth via Structural Reparameterization

Introduction

The research presented in "Rethinking Vision Transformer Depth via Structural Reparameterization" (2511.19718) addresses the computational inefficiencies of Vision Transformers (ViTs), particularly their deep architectures, by introducing a novel framework that reduces transformer layers while maintaining representational capacity. Traditional methods that focus on pruning and attention speedup don't fully address the depth-induced overhead. Instead, the proposed method introduces a branch-based structural reparameterization technique aimed at maintaining comparable accuracy with significantly fewer layers.

Vision Transformer Depth Challenge

ViTs have become a staple in computer vision, offering superior performance by modeling long-range dependencies. However, their application is hampered by the high computational cost due to their deep architectures and quadratic attention mechanisms, particularly problematic for devices with limited resources like mobile and edge platforms. As highlighted, existing small variants, such as TinyViT, still contain multiple layers (e.g., 12 layers) and millions of parameters, falling short of being truly efficient for real-time applications on resource-constrained devices.

Proposed Method: Structural Reparameterization

The authors propose a method that leverages parallel branches during training, which are progressively merged into single-path models for inference. This structural reparameterization ensures no approximation loss at test time, a significant advantage over existing approximation-based methods.

Figure 1: Proposed Depth Compression Framework for ViTs

Key aspects of the method include:

• Branch Consolidation: The method involves merging branches at the entry points of nonlinear functions, enabling exact mathematical transformations within both FFN and MHSA modules.
• Exact Reparameterization: Ensures no accuracy degradation during inference by maintaining algebraic equivalence between training and deployment models.
• Deployment Efficiency: The resulting models, when applied to structures like ViT-Tiny, demonstrate significant speedups in mobile CPU deployments, with up to 37% reduction in latency.

Experimental Results

The work showcases empirical evaluations on ImageNet-1K where the reparameterized models achieve up to 64% improvements in throughput at isometric accuracy levels compared to their deeper counterparts. Specifically, reducing the ViT-Tiny from 12 to 3 layers still retained classification performance, indicating that extreme depth is not essential for maintaining model efficacy.

Figure 2: Effect of different Lambda Warmup Periods on ImageNet-1K D-MAE-6-R fine-tuning accuracy with linear lambda scheduler. The red point indicates the best performing warmup (10k steps).

Comparison with Related Work

This study challenges the prevalent assumption that increasing depth is necessary for improving performance in transformers. The research contrasts with past approaches that incrementally optimized deep models. By adopting a radical reduction in depth, the study opens avenues for deploying ViTs in memory-constrained environments and unconventional computing platforms like in-memory and photonic computing, where deep models are traditionally limited.

Implications and Future Directions

This paper's implications extend beyond the current landscape of hardware acceleration for transformers. By proving that ultra-shallow architectures can compete with traditionally deep models, it sets a precedent for future work on ViT efficiency. For real-time applications across diverse hardware platforms, this approach provides a new horizon of exploration for deploying machine learning at the edge.

Future research could explore further merging of modalities within the transformer architecture, expanding beyond visual tasks to encompass applications in video processing and possibly integrating computationally efficient mechanisms into emerging analog processing units.

Conclusion

The method introduced demonstrates a significant paradigm shift in constructing efficient transformer models. The findings encourage a reconsideration of existing architectural norms across computer vision research, highlighting that model depth should be re-evaluated in the context of deployment constraints and practical efficiencies. The study provides a foundational approach for structural reparameterization in ViTs, thus broadening the scope for future transformations in AI model design.