This could lead to more advanced LLMs, which rely heavily on matrix multiplication to function. According to DeepMind, these feats are just the tip of the iceberg for AlphaEvolve.

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.

I have investigated the symptoms of this in some detail but have not tried to find the cause: In short it seems like matrix multiplications with largeish numbers fails inconsistently in windows, and ...

The Holoplot X1 Matrix Array Sound System is a unique modular system that uses advanced beam-forming and other technology to deliver "previously inaccessible” audio performance to a variety of venues.

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.

MatMul-free LM removes matrix multiplications from language model architectures to make them faster and much more memory-efficient.

Abstract: We propose a high-density vertical AND-type (V-AND) flash thin-film transistor (TFT) array enabling accurate vector-matrix multiplication (VMM) operations. Compared to the planar AND-type (P ...

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, ...

We propose a high-density vertical AND-type (V-AND) flash thin-film transistor (TFT) array enabling accurate vector-matrix multiplication (VMM) operations. Compared to the planar AND-type (P-AND) ...

Click to expand... So you know enough to deny the possibility of: "Matrix multiplication advancement could lead to faster, more efficient AI models" It's pretty vague, but it is hardly nonsense.

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 ...