AMD and Intel have now published a full technical specification for ACE — AI Compute Extensions — the most significant overhaul to x86 AI compute in the architecture's history, co-authored by eight ...

ThunderKittens is a framework to make it easy to write fast deep learning kernels in CUDA. It is built around three key principles: ThunderKittens is built from the hardware up; we do what the silicon ...

In this tutorial, we explore Pyright, Microsoft’s high-performance static type checker for Python, and walk through its most powerful features in a hands-on, Colab-friendly format. We start from the ...

A simulation in magnetic resonance has the basic task of evaluating how a spin system with a certain set of interactions evolves in time, and monitors the evolution of one or more terms describing the ...

NumPy is foundational for numerical data processing in Python, providing efficient multi-dimensional array objects essential for handling datasets. It supports fast mathematical and logical operations ...

About a year ago, an AI startup known as Recogni announced a patented number system for AI math, known as Pareto. Pareto is a logarithmic system, meaning that it stores numbers using their logarithmic ...

Optical neural networks (ONNs) promise computing efficiency beyond microelectronics for modern artificial intelligence (AI). Current ONNs using analog matrix-vector multiplication (MVM) ...

⚠️ A thorough tutorial and explanation of Lie groups, Lie algebras, and geometric priors for deep learning models is beyond the scope of this article. Instead, the following sections concentrate on ...

Most linear algebra courses start by considering how to solve a system of linear equations. \[ \begin{align} a_{0,0}x_0 + a_{0,1}x_0 + \cdots a_{0,n-1}x_0 & = b_0 ...

They had to throw away most of what it produced but there was gold among the garbage. Google DeepMind has used a large language model to crack a famous unsolved problem in pure mathematics. In a paper ...