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 ...
FuriosaAI Inc., a Seoul-based developer of artificial intelligence chips, is reportedly in talks to raise a new round of funding. Sources told Bloomberg today that the startup is seeking $300 million ...
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 ...
Mesh TensorFlow (mtf) is a language for distributed deep learning, capable of specifying a broad class of distributed tensor computations. The purpose of Mesh TensorFlow is to formalize and implement ...
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 ...
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