Abstract: Multiplying matrices is among the most fundamental and compute-intensive operations in machine learning. Approximated Matrix Multiplication (AMM) based on table look-ups can significantly ...

Abstract: Alternative basis matrix multiplication algorithms are the fastest matrix multiplication algorithms in practice to date. However, are they numerically ...

Got a space-obsessed kiddo? Or maybe you’ve got a solar system project due tomorrow and no idea where to start? Either way, these Free Solar System Worksheets (PDF) are here to save the day (and maybe ...

All right, so before you download and print these recycling worksheets for your preschool or classroom…let me address the irony. Yes, it seems hypocritical to offer a school activity that has a theme ...

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, ...

Large language models such as ChaptGPT have proven to be able to produce remarkably intelligent results, but the energy and monetary costs associated with running these massive algorithms is sky high.

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, ...