The goal of Universal Numbers, or unums, is to replace IEEE floating-point with
a number system that is more efficient and mathematically consistent in
concurrent execution environments.

The motivation to replace IEEE floating-point had been brewing in the HPC
community since the late 90's as most algorithms became memory bound. The
inefficiency of IEEE floating-point has been measured and agreed upon, but it
was the AI Deep Learning community that moved first and replaced IEEE with
number systems that are tailored to the application to yield speed-ups of two
to three orders of magnitude.

The Universal library is a ready-to-use header-only library that provides
plug-in replacement for native types, and provides a low-friction environment
to start exploring alternatives to IEEE floating-point in your own algorithms.