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 ...

High-performance matrix multiplication remains a cornerstone of numerical computing, underpinning a wide array of applications from scientific simulations to machine learning. Researchers continually ...

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 ...

The automated driving developer community typically uses Eigen*, a C++ math library, for the matrix operations required by the Extended Kalman Filter algorithm. EKF usually involves many small ...

Current custom AI hardware devices are built around super-efficient, high performance matrix multiplication. This category of accelerators includes the host of AI chip startups and defines what more ...