HISA: Efficient Hierarchical Indexing for Fine-Grained Sparse Attention

1. 1Identify the bottleneck in existing sparse attention mechanisms, such as DSA, which scan the entire prefix for every query
2. 2Propose a hierarchical indexing approach to reduce the computational complexity
3. 3Implement HISA to efficiently select a subset of historical tokens for each query
4. 4Evaluate the performance of HISA in various applications, such as natural language processing tasks

AI engineers and researchers working on sparse attention mechanisms can benefit from HISA to improve the efficiency of their models, particularly in applications with long sequences

💡 HISA's hierarchical indexing approach can significantly improve the efficiency of fine-grained sparse attention mechanisms

🚀 HISA reduces the O($L^2$) bottleneck in sparse attention mechanisms!