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 ...

Floating-point arithmetic is a cornerstone of numerical computation, enabling the approximate representation of real numbers in a format that balances range and precision. Its widespread applicability ...

I am working on a viewshed* algorithm that does some floating point arithmetic. The algorithm sacrifices accuracy for speed and so only builds an approximate viewshed ...

Digital signal processors (DSPs) represent one of the fastest growing segments of the embedded world. Yet despite their ubiquity, DSPs present difficult challenges for programmers. In particular, ...

[Editor's note: For an intro to fixed-point math, see Fixed-Point DSP and Algorithm Implementation. For a comparison of fixed- and floating-point hardware, see Fixed vs. floating point: a surprisingly ...

Recently I've become involved in multiple projects where inherent limitations in floating point numbers are leading to problems (for example, carry out a few hundred matrix multiplications and the ...

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 ...

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 ...

Although something that’s taken for granted these days, the ability to perform floating-point operations in hardware was, for the longest time, something reserved for people with big wallets. This ...