Arm’s SME2 CPU extension will accelerate AI workloads on upcoming Android smartphones while Apple supports SME2 in iPads but ...

Hi, thanks for your great work on Transformer Engine! I am working on a project that requires high-performance batched matrix multiplication (i.e., 3D tensor multiplication) where all inputs are st ...

We investigate a novel approach to approximate tensor-network contraction via the exact, matrix-free decomposition of full tensor-networks. We study this method as a means to eliminate the propagat ...

CUDA Cores shine the brightest when handling tasks that benefit from parallel computation. Tensor Cores use AI to upscale graphics in video games.

We investigate the efficient combination of the canonical polyadic decomposition (CPD) and tensor hyper-contraction (THC) approaches. We first present a novel low-cost CPD solver that leverages a ...

According to Google DeepMind, AlphaEvolve has successfully discovered multiple new algorithms for matrix multiplication, surpassing the previous AlphaTensor model in efficiency and performance (source ...

The Transformer architecture, despite its scaling law, faces expensive computational cost challenges as the number of parameters increases. Quantization methods like Ternary-BERT and BitNet address ...

A fundamental operation within this domain is matrix multiplication, which underpins many computational workflows. Recent hardware innovations, like Tensor Core Units (TCUs), offer efficient ...

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.