Rigorous estimation of maximum floating-point round-off errors is an important capability
central to many formal verification tools. Unfortunately, available techniques for this task …

Automated techniques for rigorous floating-point round-off error analysis are a prerequisite
to placing important activities in HPC such as precision allocation, verification, and code …

The type length chosen for floating-point numbers (eg 32 bits or 64 bits) may have an impact
on the execution time, especially on SIMD (Single Instruction Multiple Data) units …

This article describes Salsa, an automatic tool to improve the accuracy of the floating-point
computations done in numerical codes. Based on static analysis methods by abstract …

Verified compilers such as CompCert and CakeML have become increasingly realistic over
the last few years, but their support for floating-point arithmetic has thus far been limited. In …

Round-off errors arising from the difference between real numbers and their floating-point
representation cause the control flow of conditional floating-point statements to deviate from …

Numerical software uses floating-point arithmetic to implement real-valued algorithms which
inevitably introduces roundoff errors. Additionally, in an effort to reduce energy consumption …

Minimizing the precision in which the neurons of a neural network compute is a desirable
objective to limit the resources needed to execute it. This is specially important for neural …

Elementary function calls are a common feature in numerical programs. While their
implementations in mathematical libraries are highly optimized, function evaluation is …

Precision tuning or customized precision number representations is emerging, in these
recent years, as one of the most promising techniques that make it possible to save the …