[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 is used extensively in many applications across multiple market segments. These applications often require a large number of calculations and are prevalent in financial ...

Last year, I was at the decision point. I needed to select a processor for the next version of Critical Link's MityDSP “custom-off-the-shelf” CPU platform—basically a collection of integrated building ...

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 ...

Yea, see topic. Using all floating point cut CPU usage in half.<BR><BR>I made a version of SineClock (the ancient BeOS program) for IRIX. I first wrote it with integer, since I figured that integer ...

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 ...

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, ...