Primary Algorithm : Algorithmically, Sparse-Sparse multiplication problems manifests itself in three possible forms:(a) Multiplication of a sparse matrix with a sparse diagonal, sparse block-diagonal, ...

Sparse matrix computations are prevalent in many scientific and technical applications. In many simulation applications, the solving of the sparse matrix-vector multiplication (SpMV) is critical for ...

SpMV-CNN: A set of convolutional neural nets for estimating the run time and energy consumption of the sparse matrix-vector product ...

Hefei National Laboratory for Physical Sciences at the Microscale, Department of Chemical Physics, and Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science ...

“Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures. Near-bank PIM architectures place simple cores close to DRAM banks and can yield ...

Abstract: The rising popularity of deep learning algorithms demands special accelerators for matrix-matrix multiplication. Most of the matrix multipliers are designed based on the systolic array ...

Sparse matrix computations are pivotal to advancing high-performance scientific applications, particularly as modern numerical simulations and data analyses demand efficient management of large, ...

Abstract: Sparsification technology is crucial for deploying convolutional neural networks in resource-constrained environments. However, the efficiency of sparse models is hampered by irregular ...

In this video from PASC17, Alfio Lazzaro (University of Zurich, Switzerland) presents: Increasing Efficiency of Sparse Matrix-Matrix Multiplication. “Matrix-matrix multiplication is a basic operation ...