Without a doubt, many algorithms can benefit from employing a floating-point implementation. The code can be simpler and take fewer cycles to execute than fixed-point implementations. However, these ...

[Editor's note: For an intro to floating-point math, see Tutorial: Floating-point arithmetic on FPGAs. For a comparison of fixed- and floating-point hardware, see Fixed vs. floating point: a ...

Many numerical applications typically use floating-point types to compute values. However, in some platforms, a floating-point unit may not be available. Other platforms may have a floating-point unit ...

Most of the algorithms implemented in FPGAs used to be fixed-point. Floating-point operations are useful for computations involving large dynamic range, but they require significantly more resources ...

Today’s digital signal processing applications such as radar, echo cancellation, and image processing are demanding more dynamic range and computation accuracy. Floating-point arithmetic units offer ...

In this video from the HPC Advisory Council Australia Conference, John Gustafson from the National University of Singapore presents: Beating Floating Point at its own game – Posit Arithmetic. “Dr.

Most AI chips and hardware accelerators that power machine learning (ML) and deep learning (DL) applications include floating-point units (FPUs). Algorithms used in neural networks today are often ...

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.

Floating-point arithmetic is a cornerstone of modern computational science, providing an efficient means to approximate real numbers within a finite precision framework. Its ubiquity across scientific ...