Efficient Attention

Efficient attention mechanisms aim to overcome the quadratic complexity of standard self-attention in Transformer networks, a major bottleneck for processing long sequences in various applications like natural language processing and image analysis. Current research focuses on developing faster algorithms, such as FlashAttention and its variants, and on architectural modifications like pruned token compression and linear attention via orthogonal memory, to reduce computational cost and memory footprint while maintaining accuracy. These advancements are crucial for scaling Transformer models to handle longer sequences and larger datasets, impacting fields ranging from large language models to medical image analysis and beyond.