In intelligent connected vehicle applications, tasks such as path planning and health management involve numerous matrix operations, particularly matrix multiplication. Due to limited resources, these ...

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.

We propose an efficient quantum subroutine for matrix multiplication that computes a state vector encoding the entries of the product of two matrices in superposition. The subroutine exploits ...

Java’s collections like arrays and lists are foundational building blocks. Functional programming techniques are at times the ideal way to work with these collections.

Matrix multiplication advancement could lead to faster, more efficient AI models At the heart of AI, matrix math has just seen its biggest boost "in more than a decade.” ...

Moreover, The vector-matrix multiplication cost, in the binary domain, is a major computational bottleneck for these applications. This paper introduces a novel digital in-memory stochastic computing ...

An artificial-intelligence approach known as AlphaTensor found exact matrix-multiplication algorithms that are more efficient than those previously known for many matrix sizes. The technique ...