Engineers targeting DSP to FPGAs have traditionally used fixed-point arithmetic, mainly because of the high cost associated with implementing floating-point arithmetic. That cost comes in the form of ...

In a recent survey conducted by AccelChip Inc. (recently acquired by Xilinx), 53% of the respondents identified floating- to fixed-point conversion as the most difficult aspect of implementing an ...

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 explains the basics of floating-point arithmetic, how floating-point units (FPUs) work, and how to use FPGAs for easy, low-cost floating-point processing. Inside microprocessors, numbers ...

Floating-point values contain three fields: a sign bit, exponent bits, and significand or mantissa bits. The IEEE-754 floating-point number format defined a common floating-point format that most ...

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.

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.