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 ...

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 ...

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 ...

As defined by the IEEE 754 standard, floating-point values are represented in three fields: a significand or mantissa, a sign bit for the significand and an exponent field. The exponent is a biased ...

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 ...

Although something that’s taken for granted these days, the ability to perform floating-point operations in hardware was, for the longest time, something reserved for people with big wallets. This ...

[Editor's note: For an intro to floating-point math, see Tutorial: Floating-point arithmetic on FPGAs. For a comparison of fixed- and floating-point hardware, see Fixed vs. floating point: a ...