Strictly speaking, a scalar is a 0 x 0 tensor, a vector is 1 x 0, and a matrix is 1 x 1, but for the sake of simplicity and how it relates to tensor cores in a graphics processor, we'll just deal ...

Matrix multiplication provides a series of fast multiply and add operations in parallel, and it is built into the hardware of GPUs and AI processing cores (see Tensor core). See compute-in-memory .

In DeepMind’s new algorithm, dubbed AlphaTensor, the inputs represent steps along the way to a valid matrix multiplication scheme. The first input to the neural network is the original matrix ...

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 ...

The Tensor Unit has been designed to provide much greater computation power for AI applications and, with the bulk of computations in Large Language Models (LLMs) conducted in fully-connected layers ...

Tensor for matrix multiplication and algorithms: here multiplication of 2 x 2 matrices. Entries equal to 1 are purple, 0 entries are semi-transparent.

Can artificial intelligence (AI) create its own algorithms to speed up matrix multiplication, one of machine learning’s most fundamental tasks? Today, in a paper published in Nature, DeepMind ...

A single number is a “rank 0” tensor. A list of numbers, called a vector, is a rank 1 tensor. A grid of numbers, or matrix, is a rank 2 tensor. And so on. But talk to a physicist or mathematician, and ...