[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 ...

Providing a broad overview of numerical computing in a historical context, this concise text takes the reader from floating point representation and correctly rounded arithmetic, to an understanding ...

Floating-point arithmetic provides a practical means of representing real numbers on digital computers by encoding them in a finite number of bits for sign, exponent and significand. The IEEE-754 ...

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.

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 ...

This article was published in Scientific American’s former blog network and reflects the views of the author, not necessarily those of Scientific American Last month, I wrote about the hype ...

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. The algorithm iteratively ...

In 1985, the Institute of Electrical and Electronics Engineers (IEEE) established IEEE 754, a standard for floating point formats and arithmetic that would become the model for practically all FP ...

Embedded C and C++ programmers are familiar with signed and unsigned integers and floating-point values of various sizes, but a number of numerical formats can be used in embedded applications. Here ...

Last year, I was at the decision point. I needed to select a processor for the next version of Critical Link's MityDSP “custom-off-the-shelf” CPU platform—basically a collection of integrated building ...