With the rapid growth of computer science and big data, the traditional von Neumann
architecture suffers the aggravating data communication costs due to the separated structure …

Training of large-scale deep neural networks is often constrained by the available
computational resources. We study the effect of limited precision data representation and …

Today's floating-point arithmetic landscape is broader than ever. While scientific computing
has traditionally used single precision and double precision floating-point arithmetics, half …

Abstract Sign Language Translation (SLT) is a challenging task due to its cross-domain
nature, involving the translation of visual-gestural language to text. Many previous methods …

Low-precision floating-point arithmetic is a powerful tool for accelerating scientific computing
applications, especially those in artificial intelligence. Here, we present an investigation …

Given the variety of numerical errors that can occur, floating-point programs are difficult to
write, test and debug. One common practice employed by developers without an advanced …

As the standard method for solving systems of linear equations, Gaussian elimination (GE) is
one of the most important and ubiquitous numerical algorithms. However, its successful use …

Do you know the difference between an optimist and a pessimist? The former believes we
live in the best possible world, and the latter is afraid that the former might be right.… In that …

As scientific computation continues to scale, efficient use of floating-point arithmetic
processors is critical. Lower precision allows streaming architectures to perform more …

HPC applications use floating point arithmetic operations extensively to solve computational
problems. Mixed-precision computing seeks to use the lowest precision data type that is …