stillwater/universal repository overview

The container contains all the tools and test suites of a successful build of the Universal library.

Universal contains a large collection of number systems providing custom arithmetic and mixed-precision algorithms for AI, Machine Learning, Computer Vision, Signal Processing, CAE, EDA, control, optimization, estimation, and approximation.

The goal of the Universal Numbers Library is to offer alternatives to native integer and floating-point for mixed-precision algorithm development and optimization. Tailoring the arithmetic types to the application's precision and dynamic range enables a new level of application performance and energy efficiency, particularly valuable for embedded applications that need autonomy through intelligent behavior.

Deep Learning algorithms in particular provide a core application vertical where alternative formats and precisions, such as half-precision floating-point and bfloat16, yield speed-ups of two to three orders of magnitude, making rapid innovation in AI possible.

The Universal Library is a ready-to-use header-only library that provides a plug-in replacement for native types and offers a low-friction environment to explore alternatives to IEEE-754 floating-point in AI, DSP, HPC, and HFT algorithms.

The basic use pattern is as simple as:

// bring in the parameterized type of interest, in this case
// a fixed-sized, arbitrary configuration classic floating-point
#include <universal/number/cfloat/cfloat.hpp>

// define your computational kernel parameterized by arithmitic type
template<typename Real>
Real MyKernel(const Real& a, const Real& b) {
    return a * b;  // replace this with your kernel computation
}

constexpr double pi = 3.14159265358979323846;

int main() {
    // if desired, create an application type alias to avoid errors
    using Real = sw::universal::half; // half-precision IEEE-754 floating-point  

    Real a = sqrt(2);
    Real b = pi;
    // finally, call your kernel with your desired arithmetic type
    std::cout << "Result: " << MyKernel(a, b) << std::endl;  
}

The library contains fast implementations of special IEEE-754 formats that do not have universal hardware implementations across x86, ARM, POWER, RISC-V, and GPUs. Special formats such as quarter precision, quarter, half-precision, half, and quad precision, quad, are provided, as well as vendor-specific extensions, such as NVIDIA TensorFloat, Google's Brain Float, bfloat16, or TI DSP fixed-points, fixpnt. In addition to these often used specializations, Universal supports static and elastic integers, decimals, fixed-points, rationals, linear floats, tapered floats, logarithmic, interval, and adaptive-precision integers, rationals, and floats.