Replacing computationally complex floating-point tensor multiplication with the much simpler integer addition is 20 times more efficient. Together with incoming hardware improvements this promises ...

Artificial intelligence grows more demanding every year. Modern models learn and operate by pushing huge volumes of data through repeated matrix operations that sit at the heart of every neural ...

Familiarity with linear algebra is expected. In addition, students should have taken a proof-based course such as CS 212 or Math 300. Tensors, or multiindexed arrays, generalize matrices (two ...

New Linear-complexity Multiplication (L-Mul) algorithm claims it can reduce energy costs by 95% for element-wise tensor multiplications and 80% for dot products in large language models. It maintains ...

The cover shows an artistic impression of a matrix multiplication tensor — a 3D array of numbers — in the process of being solved by deep learning. Efficient matrix multiplication algorithms can help ...

A processing unit in an NVIDIA GPU that accelerates AI neural network processing and high-performance computing (HPC). There are typically from 300 to 600 Tensor cores in a GPU, and they compute ...