Right off the bat, let’s give a shout out to the mathematician propeller-heads who create the transformations that make it possible to do all kinds of high performance computing to simulate, model, ...
AI adoption is reaching an inflection point as the focus shifts from training new models to serving them. For the AI startups vying for a slice of Nvidia's pie, it's now or never. Compared to training ...
Abstract: General matrix-matrix multiplication (GEMM), serving as a cornerstone of AI computations, has positioned tensor processing engines (TPEs) as increasingly critical components within existing ...
Here is how you know that GenAI training and GenAI inference are very different computing and networking beasts, and diverging more with each passing day: Google has just forked its Tensor Processing ...
Double precision floating point computation (aka FP64) is what keeps modern aircraft in the sky, rockets going up, vaccines effective, and, yes, nuclear weapons operational. But rather than building ...
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 ...
A team at Stanford has shown that large language models can automatically generate highly efficient GPU kernels, sometimes outperforming the standard functions found in the popular machine learning ...
Google’s DeepMind research division claims its newest AI agent marks a significant step toward using the technology to tackle big problems in math and science. The system, known as AlphaEvolve, is ...
AlphaEvolve uses large language models to find new algorithms that outperform the best human-made solutions for data center management, chip design, and more. Google DeepMind has once again used large ...
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