[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 ...

Floating-point arithmetic provides a practical means of representing real numbers on digital computers by encoding them in a finite number of bits for sign, exponent and significand. The IEEE-754 ...

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 ...

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 ...

The term floating point is derived from the fact that there is no fixed number of digits before and after the decimal point; namely, the decimal point can float. There are also representations in ...

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 ...

The traditional view is that the floating-point number format is superior to the fixed-point number format when it comes to representing sound digitally. In fact, while it may be counter-intuitive, ...

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 ...