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 ...

[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 ...

Once the domain of esoteric scientific and business computing, floating point calculations are now practically everywhere. From video games to large language models and kin, it would seem that a ...

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 ...

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 ...

Wong: EEMBC has been around for more than 16 years, why the recent need for floating-point benchmarks? Levy: While floating-point units in processors have been in use for decades, in recent times ...

While the media buzzes about the Turing Test-busting results of ChatGPT, engineers are focused on the hardware challenges of running large language models and other deep learning networks. High on the ...

When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works. The traditional view is that the floating-point number format is superior to the fixed-point ...