BLASST: Dynamic BLocked Attention Sparsity via Softmax Thresholding

This article introduces BLASST, a method for achieving dynamic blocked attention sparsity using softmax thresholding. The focus is on improving the efficiency of attention mechanisms in large language models (LLMs). The approach likely aims to reduce computational costs by selectively activating attention weights. Further details on the specific implementation, performance gains, and limitations would be needed for a complete analysis.