FP-ANR: A representation format to handle floating-point cancellation at run-time

Abstract

When dealing with floating-point numbers, there are several sources of error which can drastically reduce the numerical quality of computed results. One of those error sources is the loss of significance or cancellation, which occurs during for example, the subtraction of two nearly equal numbers. In this article, we propose a representation format named Floating-Point Adaptive Noise Reduction (FP-ANR). This format embeds cancellation information directly into the floating-point representation format thanks to a dedicated pattern. With this format, insignificant trailing bits lost during cancellation are removed from every manipulated floating-point number. The immediate consequence is that it increases the numerical confidence of computed values. The proposed representation format corresponds to a simple and efficient implementation of significance arithmetic based and compatible with the IEEE Standard 754 standard.

Automatic Exploration of Reduced Floating-Point Representations in Iterative Methods

“…Intel Vector Neural Network Instruction), novel FP representations (e.g. BFloat [11], posits [18], fpanr [12]), and libraries for approximate computing [22].…”