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 ...

This project is a hardware matrix multiplication accelerator that computes C = A × B for N×N matrices (currently only tested for NxN matrices, however the design is parameterized and with small ...

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 ...

Abstract: This paper investigates sparse matrix-vector (SpMV) multiplication algorithm performance for unstructured sparse matrices. The development of an SpMV multiplication algorithm for this type ...

Abstract: We present a Mathematics of Arrays (MoA) and ψ-calculus derivation of the memory-optimal operational normal form for ELLPACK sparse matrix-vector multiplication (SpMV) on GPUs. Under the ...

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