[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 ...

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 ...

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 ...

Based on recent technological developments, high-performance floating-point signal processing can, for the very first time, be easily achieved using FPGAs. To date, virtually all FPGA-based signal ...

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 ...

Xilinx has announced the availability of ISE Design Suite 13.3 featuring new capabilities for DSP designers to implement bit-accurate single, double and full custom precision floating-point math ...

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 ...

In this video, John Gustafson from the National University of Singapore presents: Beyond Floating Point: Next Generation Computer Arithmetic. “A new data type called a “posit” is designed for direct ...

Artificial intelligence (AI) has become pervasive in our lives, improving our phones, cars, homes, medical centers, and more. As currently structured, these models primarily run in power-hungry, ...

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 ...