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 ...

Discovering faster algorithms for matrix multiplication remains a key pursuit in computer science and numerical linear algebra. Since the pioneering contributions of Strassen and Winograd in the late ...

Since homomorphic encryption enables SIMD operations by packing multiple values into a vector of operations and enabling pairwise addition or multiplication operations, one (old) conventional method ...

A new technical paper titled “Scalable MatMul-free Language Modeling” was published by UC Santa Cruz, Soochow University, UC Davis, and LuxiTech. “Matrix multiplication (MatMul) typically dominates ...

A team of software engineers at the University of California, working with one colleague from Soochow University and another from LuxiTec, has developed a way to run AI language models without using ...

Presenting an algorithm that solves linear systems with sparse coefficient matrices asymptotically faster than matrix multiplication for any ω > 2. Our algorithm can be viewed as an efficient, ...

The original version of this story appeared in Quanta Magazine. Moore’s law is already pretty fast. It holds that computer chips pack in twice as many transistors every two years or so, producing ...

Matrix multiplication (MatMul) is a fundamental operation in most neural networks, primarily because GPUs are highly optimized for these computations. Despite its critical role in deep learning, ...