Convolutional neural networks (CNNs) are becoming more and more important for solving
challenging and critical problems in many fields. CNN inference applications have been …

Neural networks (NNs) are increasingly employed in safety-critical domains and in
environments prone to unreliability (eg, soft errors), such as on spacecraft. Therefore, it is …

General Matrix Multiplication (GEMM) is a crucial algorithm for various applications such as
machine learning and scientific computing since an efficient GEMM implementation is …

K-means is a widely used algorithm in clustering, how-ever, its efficiency is primarily
constrained by the computational cost of distance computing. Existing implementations …

Every algorithm has its own best-case as well as its worst-case scenario, so it is difficult to
determine the best sorting algorithm just by its Big-O. Not only that, the amount of memory …

Basic Linear Algebra Subprograms (BLAS) is a core library in scientific computing and
machine learning. This paper presents FT-BLAS, a new implementation of BLAS routines …

Data reduction techniques have been widely demanded and used by large-scale high
performance computing (HPC) applications because of vast volumes of data to be produced …

One-sided dense matrix decompositions (eg, Cholesky, LU, and QR) are the key
components in scientific computing in many different fields. Although their design has been …

Basic Linear Algebra Subprograms (BLAS) serve as a foundational library for scientific
computing and machine learning. In this article, we present a new BLAS implementation, FT …

Vision Transformers (ViTs) with outstanding performance becomes a popular backbone of
deep learning models for the main-stream vision tasks including classification, object …