Dozens of machine learning algorithms require computing the inverse of a matrix. Computing a matrix inverse is conceptually easy, but implementation is one of the most challenging tasks in numerical ...

Either way, let’s not be in denial about it. Credit...Illustration by Christoph Niemann Supported by By Kevin Roose and Casey Newton Kevin Roose and Casey Newton are the hosts of The Times’s “Hard ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of computing a matrix inverse using the Newton iteration algorithm. Compared to other algorithms, Newton ...

Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.

Abstract: This paper presents a Carbon Nanotube FET-based ternary matrix multiplication using systolic array architecture for applications towards ternary neural networks and image processing ...

Parallel Computing starter project to build GPU & CPU kernels in CUDA & C++ and call them from Python without a single line of CMake using PyBind11 ...