A recent article [2] introduces a symbolic testing approach that accurately constructs and propagates floating-point constraints to reveal hidden errors not readily detected by traditional methods.

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 ...

Although floating point arithmetic standards – like the commonly used IEEE 754 – seek to minimize this error, it’s inevitable that across the range of a floating point variable loss of ...

The End of Error: Unum Computing by [John L. Gustafson] begins his case for a superset of floating point arithmetic with a simple number system of integers expressed with just five bits.

Our whitepaper compares the efficiency of floating point and integer quantization. For training, the floating-point formats FP16 and FP32 are commonly used as they have high enough accuracy, and ...

"Dr. Gustafson has recently finished writing a book, The End of Error: Unum Computing, that presents a new approach to computer arithmetic: the unum. The universal number, or unum format, encompasses ...

What Every Computer Scientist Should Know About Floating-Point Arithmetic was posted a few months ago when someone asked a similar question.

Floating-point arithmetic is necessary to meet precision and performance requirements for an increasing number of applications. Today, most 32-bit embedded processors that offer this functionality are ...

Because fixed-point DSPs are consumed in large volumes, their price per chip is a fraction of the price of a floating-point DSP. As a result, the only developers who can reasonably justify using a ...