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

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 ...

Deep-learning models perform operations on tensors using repeated matrix multiplication and addition—this process is how neural networks learn complex patterns in data.

The new version of AlphaZero discovered a faster way to do matrix multiplication, a core problem in computing that affects thousands of everyday computer tasks.

Matrix multiplication is at the heart of many machine learning breakthroughs, and it just got faster—twice. Last week, DeepMind announced it discovered a more efficient way to perform matrix ...

Today, DeepMind unveiled AlphaTensor, the “first artificial intelligence system" to shed light on a 50-year-old open question in mathematics.

Photonic accelerators have been widely designed to accelerate some specific categories of computing in the optical domain, especially matrix multiplication, to address the growing demand for ...