Abstract: Sparse matrix multiplication is one of the key computational kernels in large-scale data analytics. However, a naive implementation suffers from the overheads of irregular memory accesses ...

Abstract: Sparse Matrix-Matrix Multiplication (SpMM) is one of the key operators in many fields, showing dynamic features in terms of sparsity, element distribution, and data dependency. Previous ...

This work stems from the 'FPGA 101: From Reconfigurable to Domain-Specific Systems' course attended in fall '24 at NECSTLab, under the supervision of Asst. Prof. Davide Conficconi and Giuseppe ...

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

“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 ...

A novel AI-acceleration paper presents a method to optimize sparse matrix multiplication for machine learning models, particularly focusing on structured sparsity. Structured sparsity involves a ...

A real-world matrix (1138_bus.mtx) is used to benchmark performance across different execution models. ├── CMakeLists.txt ├── include/ │ ├── csr_matrix.hpp │ ├── csr_operations.hpp │ └── ...