Optimizing Deep Neural Networks with N :M Structured Sparsity

Abstract

Deep neural networks (DNNs) have grown increasingly large and complex, which requires effective optimization techniques to improve efficiency and scalability. Sparsity has emerged as a primary and widely adopted optimization approach, enabling significant reductions in DNN computational demands while preserving model performance. Specifically, structured N:M sparsity has emerged as a promising approach due to its alignment with modern hardware architectures, allowing for efficient model compression and computations