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.

Digital signal processors (DSPs) represent one of the fastest growing segments of the embedded world. Yet despite their ubiquity, DSPs present difficult challenges for programmers. In particular, ...

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 ...

Why floating point is important for developing machine-learning models. What floating-point formats are used with machine learning? Over the last two decades, compute-intensive artificial-intelligence ...

Native Floating-Point HDL code generation allows you to generate VHDL or Verilog for floating-point implementation in hardware without the effort of fixed-point conversion. Native Floating-Point HDL ...

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 ...

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 ...

Here we provide rational for using Centar’s floating-point IP core for the new Altera Arria 10 and Stratix 10 FPGA platforms. After a short contextual discussion section, a comparison of various FFT ...

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 ...