Abstract: Even though the task of multiplying matrices appears to be rather straightforward, it can be quite challenging in practice. Many researchers have focused on how to effectively multiply two 2 ...

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...

I made a program that reads CSV data and analyzes it. The program calculates averages and creates different charts like bar graphs, scatter plots, and heatmaps to visualize the data. I built a machine ...

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 ...

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.

At its Google I/O developer conference, Google on Tuesday announced the next generation of its Tensor Processing Units (TPU), its data center AI chips. This sixth generation of chips, dubbed Trillium, ...

PyTorch introduced TK-GEMM, an optimized Triton FP8 GEMM kernel, to address the challenge of accelerating FP8 inference for large language models (LLMs) like Llama3 using Triton Kernels. Standard ...

Some things remain unmappable, and finding the NFL franchise guy is one of them. Ask any generative AI, matrices-multiplying algorithmic gizmo which quarterback to take in the first round of this ...