Tom's Hardware on MSN

SK hynix and TetraMem collaborate on experimental chip to bolster edge AI energy efficiency

SK hynix, TetraMem, and the University of Southern California built a memristor-based in-memory computing system-on-chip for ...
Electropages

New AI Instructions for x86 Architectures Announced

Intel and AMD have jointly announced ACE, a new x86 instruction set extension that brings dedicated AI acceleration to CPUs, ...
IEEE

Vertical AND-Type Flash TFT Array Capable of Accurate Vector-Matrix Multiplication Operations for Hardware Neural Networks

Abstract: We propose a high-density vertical AND-type (V-AND) flash thin-film transistor (TFT) array enabling accurate vector-matrix multiplication (VMM) operations. Compared to the planar AND-type (P ...
IEEE

Over-the-Air Distributed Matrix-Vector Multiplication With Analog Coding

Abstract: Distributed matrix-vector multiplication plays a key role in numerous computing-intensive applications, including machine learning, by leveraging distributed computing resources known as ...
The New York Times

Here’s Why ‘The Matrix’ Is More Relevant Than Ever

One scene reflects the themes — A.I., fake news, transgender lives and Gen X — that make the film a classic. By Alissa Wilkinson Neo, the hero of “The Matrix,” is sure he lives in 1999. He has a green ...
Ars Technica

Researchers upend AI status quo by eliminating matrix multiplication in LLMs

Researchers claim to have developed a new way to run AI language models more efficiently by eliminating matrix multiplication from the process. This fundamentally redesigns neural network operations ...
techxplore

Multiplexed neuron sets make smaller optical neural networks possible

Seeking to improve the practicality of optical neural networks that use wavelength division multiplexing, a research team developed a structure called multiplexed neuron sets and a corresponding ...
Ars Technica

Matrix multiplication advancement could lead to faster, more efficient AI models

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 ...