Tensordyne says logarithmic computing could reduce AI inference costs and power demands, offering an alternative to conventional chip designs.
Computer scientists at UC Berkeley say that AI models show promise as a way to discover and optimize algorithms. In a preprint paper titled "Barbarians at the Gate: How AI is Upending Systems Research ...
Abstract: Matrix multiplication is a fundamental computational operation widely used in various engineering applications. To accelerate large-scale matrix multiplication, computing tasks are commonly ...
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 ...
nvmath-python brings the power of the NVIDIA math libraries to the Python ecosystem. The package aims to provide intuitive pythonic APIs giving users full access to all features offered by NVIDIA's ...
Abstract: Coded computing is an effective technique to mitigate “stragglers” in large-scale and distributed matrix multiplication. In particular, univariate polynomial codes have been shown to be ...
Everything on a computer is at its core a binary number, since computers do everything with bits that represent 0 and 1. In order to have a file that is "plain text", so human readable with minimal ...
As transformer models grow in size and complexity, they face significant challenges in terms of computational efficiency and memory usage, particularly when dealing with long sequences. Flash ...
A handy open source tool for packaging up LLMs into single universal chatbot executables that are easy to distribute and run has apparently had a 30 to 500 percent CPU performance boost on x86 and Arm ...
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