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 ...

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 ...

Optical computing uses photons instead of electrons to perform computations, which can significantly increase the speed and energy efficiency of computations by overcoming the inherent limitations of ...

Researchers from the Institute for Artificial Intelligence at Peking University, led by Sun Zhong, have developed a high-precision and scalable analogue matrix computing chip based on RERAM, which ...

Sparse matrix-vector multiplication (SpMV) is a fundamental opera- tion in numerous applications such as scientific computing, machine learning, and graph analytics. While recent studies have made ...