Sparse Matrix Multiplication

Sparse matrix multiplication (SpMM) focuses on efficiently performing matrix multiplication when the matrices contain a high proportion of zero entries, a common occurrence in many applications. Current research emphasizes developing optimized algorithms and hardware implementations tailored to various sparsity patterns (e.g., butterfly, N:M, block sparse), often leveraging machine learning techniques for adaptive dataflow selection and efficient memory management. These advancements are crucial for accelerating computations in deep learning (especially transformer models and graph neural networks), scientific computing, and other fields where large-scale sparse data is prevalent, leading to significant improvements in speed and energy efficiency.