General Sparse Matrix-Matrix Multiplication (SpGEMM) has attracted much attention from
researchers in graph analyzing, scientific computing, and deep learning. Many optimization …

Sparsity is a growing trend in modern DNN models. Existing Sparse-Sparse Matrix
Multiplication (SpMSpM) accelerators are tailored to a particular SpMSpM dataflow (ie, Inner …

The widespread use of Sparse Matrix Dense Matrix Multiplication (SpMM) and Sampled
Dense Matrix Dense Matrix Multiplication (SDDMM) kernels makes them candidates for …

Recent years have seen an increasing number of Graph Convolutional Network (GCN)
models employed in various real-world applications. However, designing efficient …

Sparsity has become one of the promising methods to compress and accelerate Deep
Neural Networks (DNNs). Among different categories of sparsity, structured sparsity has …

N: M Structured sparsity has garnered significant interest as a result of relatively modest
overhead and improved efficiency. Additionally, this form of sparsity holds considerable …

Sparse matrices are the key ingredients of several application domains, from scientific
computing to machine learning. The primary challenge with sparse matrices has been …

Graph Convolutional Networks (GCNs) have shown much promise in resolving
sophisticated scientific problems with non-Euclidean data, such as traffic prediction, disease …

The multiplication of sparse matrix and vector (SpMV) is one of the most widely used kernels
in high-performance computing as well as machine learning acceleration for sparse neural …

Sparse tensor-times-vector (SpTV) is the core computation of tensor decomposition.
Optimizing the computational performance of SpTV on CPU-GPU becomes a challenge due …