What do encrypted messages, recognizing speech commands and running simulations to predict the weather have in common? They all rely on matrix multiplication for accurate calculations. DeepMind, an ...

The company revealed on 5 October that its AI software had beaten a record that had stood for more than 50 years for the matrix multiplication problem – a common operation in all sorts of ...

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

The standard “back-propagation” training technique for deep neural networks requires matrix multiplication, an ideal workload for GPUs. With SLIDE, Shrivastava, Chen and Medini turned neural network ...

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 ...

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

Karatsuba’s divide-and-conquer multiplication algorithm takes advantage of this saving. Consider a multiplication algorithm that parallels the way multiplication of complex numbers works.

A new method, called the QZ algorithm, is presented for the solution of the matrix eigenvalue problem Ax = λ Bx with general square matrices A and B. Particular attention is paid to the degeneracies ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of computing a matrix inverse using the Newton iteration algorithm. Compared to other algorithms, Newton ...