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.

Researchers upend AI status quo by eliminating matrix multiplication in LLMs Running AI models without floating point matrix math could mean far less power consumption.

Matrix multiplication (MatMul) is a fundamental operation in most neural networks, primarily because GPUs are highly optimized for these computations. Despite its critical role in deep learning, ...

algorithms New Breakthrough Brings Matrix Multiplication Closer to Ideal By eliminating a hidden inefficiency, computer scientists have come up with a new way to multiply large matrices that’s faster ...

Using NumPy for array and matrix math in Python Many mathematical operations, especially in machine learning or data science, involve working with matrixes, or lists of numbers.

Modern GPUs commonly employ specialized matrix multiplication units (MXUs) to accelerate matrix multiplication, the core computation of deep learning workloads. However, it is challenging to exploit ...

Implementing 3D shape transformations using matrix multiplication and a basic line scan-conversion algorithm. In order to run the main program, you must have a version of Python that is 3.6+ and have ...

What do encrypted messages, recognizing speech commands and running simulations to predict the weather have in common? They all rely on matrix multiplication for accurate calculations. DeepMind, an ...