Efficient Transformer

Efficient Transformers aim to overcome the computational limitations of standard Transformers, particularly their quadratic complexity with respect to sequence length, while maintaining high performance across diverse tasks. Current research focuses on developing novel attention mechanisms (e.g., linear attention, sparse attention, and approximate attention), optimized architectures (incorporating CNNs, hierarchical structures, and prototype-based methods), and efficient training strategies (like dynamic layer tying and low-precision arithmetic). These advancements are crucial for deploying Transformers on resource-constrained devices and expanding their applicability to large-scale datasets and real-time applications in fields like computer vision, natural language processing, and robotics.