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 ...

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 units (fpu) can increase the range and precision of mathematical calculations or enable greater throughput in less time, making it easier to meet real time requirements. Or, by enabling ...

I don’t know about you, but I typically have a number of “back-burner” projects on the go. Currently I'm playing with creating my own simple binary floating-point format as part of an educational tool ...

Many numerical applications typically use floating-point types to compute values. However, in some platforms, a floating-point unit may not be available. Other platforms may have a floating-point unit ...

This paper discusses a simple and effective method for the summation of long sequences of floating point numbers. The method comprises two phases: an accumulation phase where the mantissas of the ...

There is a natural preference to use floating-point implementations in custom embedded applications because they offer a much higher dynamic range and as a byproduct bypass the design hassle of ...

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 ...