SANTA CLARA, Calif.--(BUSINESS WIRE)--Further expanding SiFive’s lead in RISC-V AI IP, the company today launched its 2nd Generation Intelligence™ family, featuring five new RISC-V-based products ...

Multiplication in Python may seem simple at first—just use the * operator—but it actually covers far more than just numbers. You can use * to multiply integers and floats, repeat strings and lists, or ...

IBM's new Power11 servers aim to address growing modernization needs in the enterprise datacenter, expanding the Power lineup to cover new use cases. IBM recently achieved its highest market cap ever.

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...

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 ...

Discovering faster algorithms for matrix multiplication remains a key pursuit in computer science and numerical linear algebra. Since the pioneering contributions of Strassen and Winograd in the late ...

AlphaEvolve uses large language models to find new algorithms that outperform the best human-made solutions for data center management, chip design, and more. Google DeepMind has once again used large ...

Abstract: While the Karatsuba algorithm reduces the complexity of large integer multiplication, the extra additions required minimize its benefits for smaller integers of more commonly-used bitwidths.

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.

Matrix multiplication is a fundamental operation in linear algebra, but its behavior can seem a bit strange at first. The key to understanding it lies in understanding how the dimensions of the ...