Photonic innovation: researchers in the US have created an optical metamaterial that can perform vector–matrix multiplication. (Courtesy: iStock/Henrik5000) A new silicon photonics platform that can ...

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 ...

There has been an ever-growing demand for artificial intelligence and fifth-generation communications globally, resulting in very large computing power and memory requirements. The slowing down or ...

“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 ...

Nearly all big science, machine learning, neural network, and machine vision applications employ algorithms that involve large matrix-matrix multiplication. But multiplying large matrices pushes the ...

If \(A\) is a \(3\times 3\) matrix then we can apply a linear transformation to each rgb vector via matrix multiplication, where \([r,g,b]\) are the original values ...

It is shown that a class of infinite, block-partitioned, stochastic matrices has a matrix-geometric invariant probability vector of the form (x0,x1,...), where xk=x0Rk, for k≥ 0. The rate matrix R is ...