Block Sparsity

Block sparsity leverages the clustered distribution of non-zero elements in data or model parameters to improve efficiency and reduce computational costs in various machine learning tasks. Current research focuses on developing algorithms and architectures that exploit this structure, including sparse activation methods for large language models and vision transformers, block-structured pruning techniques for efficient model compression, and hardware-aware optimization for accelerated inference. These advancements are significant because they enable the training and deployment of larger, more complex models while mitigating the computational and memory burdens associated with high-dimensionality, leading to improved performance and reduced energy consumption in diverse applications.