Posits and the state of numerical representations in the age of exascale and edge computing

Abstract

Growing constraints on memory utilization, power consumption, and I/O throughput have increasingly become limiting factors to the advancement of high performance computing (HPC) and edge computing applications. IEEE-754 floating-point types have been the de facto standard for floating-point number systems for decades, but the drawbacks of this numerical representation leave much to be desired. Alternative representations are gaining traction, both in HPC and machine learning environments. Posits have recently been proposed as a drop-in replacement for the IEEE-754 floating-point representation. We survey the state-of-the-art and state-of-the-practice in the development and use of posits in edge computing and HPC. The current literature supports posits as a promising alternative to traditional floating-point systems, both as a stand-alone replacement and in a mixed-precision environment. Development and standardization of the posit type is ongoing, and much research remains to explore the application of posits in different domains, how to best implement them in hardware, and where they fit with other numerical representations.