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.

The process and progress of automating algorithmic discovery First, they converted the problem of finding efficient algorithms for matrix multiplication into a single-player game. In this game, the ...

Matrix multiplications are used to describe transformations in space and the matrices represent a mathematical concept called a tensor, the general term for scalars and vectors.

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 ...

Oct 06, 2022 11:20:00 The strongest shogi AI reaches new ground, DeepMind's AI 'AlphaTensor' succeeds in improving the matrix multiplication algorithm that has been stagnant for over 50 years ...

Liqid Matrix software delivers disaggregated composability for NVIDIA A100 Tensor Core GPUs with increased utilization, flexibility, and time to value for AI+ML, HPC, and other high-value ...

Google says its fourth-generation TPU offers more than double the matrix multiplication TFLOPs of a third-generation TPU, where a single TFLOP is equivalent to 1 trillion floating-point operations ...