Using Semi-Structured Sparsity of Activations to Accelerate Neural Networks

The article provides a comprehensive study of semi-structured sparsity methods aimed at optimizing deep neural networks. The main focus is on comparing two strategies - N:M (2:4) and block (4×4) sparsity - in different architectures. Experiments show that these approaches provide significant acceleration of model output without significant loss of accuracy. The results obtained confirm the prospects of semi-structured sparsity for deploying neural networks in conditions of limited computing power.