Why floating point is important for developing machine-learning models. What floating-point formats are used with machine learning? Over the last two decades, compute-intensive artificial-intelligence ...

An unfortunate reality of trying to represent continuous real numbers in a fixed space (e.g. with a limited number of bits) is that this comes with an inevitable loss of both precision and accuracy.

Multiplication on a common microcontroller is easy. But division is much more difficult. Even with hardware assistance, a 32-bit division on a modern 64-bit x86 CPU can run between 9 and 15 cycles.

AI is all about data, and the representation of the data matters strongly. But after focusing primarily on 8-bit integers and 32‑bit floating-point numbers, the industry is now looking at new formats.

Before becoming adopted as a catch-phrase on TikTok, the term ‘flop’ was a short-hand for floating point operations per second. Floating point numbers are commonly known as “real” numbers and, in the ...

An iconic example of this is the failure of the first Ariane 5 launch in 1996. Just 37 seconds after launch, the rocket left its trajectory began to spin uncontrollably and eventually self-destructed.

While the media buzzes about the Turing Test-busting results of ChatGPT, engineers are focused on the hardware challenges of running large language models and other deep learning networks. High on the ...

One of the common tasks you'll encounter when scripting in Bash is performing arithmetic operations on variables, particularly division. This process might seem straightforward, but it requires ...