Data intensive computations in data centers are performed by an increasingly popular programming framework named MapReduce. An advantage of this framework is that the algorithm is divided into simple ...

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 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 ...

Multiplying Matrices Matrix multiplication is one of the most fundamental and ubiquitous operations in all of mathematics. To multiply a pair of n -by- n matrices, each with n2 elements, you multiply ...

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 ...

A new research paper titled “Discovering faster matrix multiplication algorithms with reinforcement learning” was published by researchers at DeepMind. “Here we report a deep reinforcement learning ...

DeepMind AI can multiply quicker than ever imagined, beating a previous record The newest version of this AI has broken a 50-year record in matrix multiplication.