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

New lower values for p get discovered all the time (maybe once a year). It is conjectured that they will approach 2.0 without ever getting quite to it. Somehow Quanta Mag heard about the new result ...

Distributed computing has markedly advanced the efficiency and reliability of complex numerical tasks, particularly matrix multiplication, which is central to numerous computational applications from ...

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 ...

Multiplying the content of two x-y matrices together for screen rendering and AI processing. Matrix multiplication provides a series of fast multiply and add operations in parallel, and it is built ...

AI training time is at a point in an exponential where more throughput isn't going to advance functionality much at all. The underlying problem, problem solving by training, is computationally ...