Beyond MACs: Hardware Efficient Architecture Design for Vision Backbones
Summary: arXiv:2603.26551v1 Announce Type: cross
Abstract: Vision backbone networks play a central role in modern computer vision. Enhancing their efficiency directly benefits a wide range of downstream applications. To measure efficiency, many publications rely on MACs (Multiply Accumulate operations) as a predictor of execution time. In this paper, we experimentally demonstrate the shortcomings of such a metric, especially in the context of edge devices. By contrasting the MAC count and execution time of common architectural design elements, we identify key factors for efficient execution and provide insights to optimize backbone design.
Introduction
Modern computer vision applications rely heavily on backbone networks, which serve as the foundational architecture for processing visual information. As the demand for real-time performance increases, optimizing these networks for efficiency becomes paramount. Traditionally, the efficiency of these networks has been measured in terms of Multiply Accumulate operations (MACs). However, this paper highlights the limitations of using MACs as a sole metric for assessing efficiency, particularly in edge device contexts.
Limitations of MACs
Our research reveals several critical shortcomings associated with MACs:
- MACs do not account for the actual execution time on varying hardware platforms.
- Different architectural components can have vastly different execution times, regardless of their MAC count.
- Reliance on MACs can lead to misleading conclusions about the efficiency of a network.
Key Factors for Efficient Execution
Through our experimental analysis, we identified several key factors that influence the execution efficiency of vision backbones:
- Data flow and memory access patterns significantly impact performance.
- The choice of activation functions can reduce computational overhead.
- Layer designs, including their interconnections, play a crucial role in overall efficiency.
Introducing LowFormer
Based on our findings, we introduce LowFormer, a novel family of vision backbones designed with a focus on efficiency. Key features of LowFormer include:
- Lowtention: A lightweight alternative to Multi-Head Self-Attention that enhances computational efficiency.
- A streamlined design that balances macro and micro architectural elements for optimal performance.
- Proven effectiveness, achieving superior results on ImageNet while significantly reducing execution times.
Performance Evaluation
We evaluated LowFormer on various hardware platforms, including edge GPUs and desktop GPUs. Our findings indicate:
- LowFormer consistently outperforms recent state-of-the-art backbones with remarkable speed-ups.
- It demonstrates wide applicability across various tasks, including:
- Image classification
- Object detection
- Semantic segmentation
- Image retrieval
- Visual object tracking
Conclusion
In conclusion, while MACs have been a traditional metric for measuring the efficiency of vision backbones, our research underscores their limitations. By introducing LowFormer and its innovative design features, we pave the way for more efficient execution in computer vision applications. Our code and models are available for further exploration at LowFormer GitHub Repository.