“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 ...

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 ...

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

I'm getting ready to start working on a C/C++ project that will be building and solving large tri-diagonal, block tri-diagonal and triangular matrices. I know there are a lot of libraries available ...

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 ...

We consider the model y = Xθ* + ξ, Z = X + Ξ, where the random vector y ∈ ℝ n and the random n × p matrix Z are observed, the n × p matrix X is unknown, Ξ is an n × p random noise matrix, ξ ∈ ℝ n is a ...

We introduce a new sparse sliced inverse regression estimator called Cholesky matrix penalization, and its adaptive version, for achieving sparsity when estimating the dimensions of a central subspace ...