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 ...

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

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 ...

Computer scientists have discovered a new way to multiply large matrices faster than ever before by eliminating a previously unknown inefficiency, reports Quanta Magazine. This could eventually ...

Mathematicians love a good puzzle. Even something as abstract as multiplying matrices (two-dimensional tables of numbers) can feel like a game when you try to find the most efficient way to do it.

This project implements various secure matrix multiplication algorithms using the CKKS (Cheon-Kim-Kim-Song) homomorphic encryption scheme through OpenFHE library. It includes benchmarking tools, tests ...

The project implements a 2D matrix multiplication accelerator based on a systolic array architecture. The module design is written in Verilog, and verification testbenches are written in SystemVerilog ...