Attention Computation

Attention computation, a core operation in Transformer-based models, is crucial for processing sequential data but suffers from quadratic time complexity, hindering scalability to long sequences. Current research focuses on developing faster alternatives, including approximate nearest neighbor search methods, random feature approximations of attention kernels, and various pruning and sparsity techniques applied to both the attention matrix and model architectures. These efforts aim to improve the efficiency of large language and vision-language models, enabling real-time applications and reducing computational costs for tasks involving long sequences.