Block SkiM

Block-Skim refers to a family of techniques aiming to improve the efficiency and performance of various machine learning models by selectively processing only the most relevant information. Current research focuses on developing algorithms that identify and "skim" unimportant data points, whether tokens in language models, frames in video analysis, or sections of audio, using methods like attention-weight analysis and layer-wise skimming. These advancements lead to significant speedups and reduced computational costs in applications ranging from question answering and speech separation to vulnerability detection in online forums, ultimately impacting both the efficiency and scalability of numerous machine learning systems.