Research on compressing Vision Transformer models for efficient deployment on mobile and edge devices using advanced pruning techniques.
- Single-path one-shot neural architecture search for ViT compression
- Structured pruning with fine-tuning optimization
- 35% reduction in parameters and FLOPs while maintaining accuracy
- Optimized for mobile and edge device deployment
- Focus on practical AI model efficiency and accessibility
Technical implementation details and benchmarking results coming soon.
Vision Transformers achieve excellent performance but their computational requirements limit deployment on resource-constrained devices. Our research addresses this gap by developing efficient compression techniques that maintain model quality while dramatically reducing computational costs.
The 35% parameter reduction makes state-of-the-art vision models relatively more accessible for real-world mobile applications.
Detailed compression methodology and performance analysis will be shared soon.