A custom-built chip for machine learning from Google. Introduced in 2016 and found only in Google datacenters, the Tensor Processing Unit (TPU) is optimized for matrix multiplications, which are ...

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 ...

Dan Fleisch briefly explains some vector and tensor concepts from A Student’s Guide to Vectors and Tensors. In the field of machine learning, tensors are used as representations for many applications, ...

The Tensor G2's AI acceleration enables features like processing photos and translating languages. With it, converting speech to text is 70% faster. Stephen Shankland worked at CNET from 1998 to 2024 ...

For the past five years, Google Pixel phones have been powered by an in-house system-on-a-chip (SoC) designed by Google. The company's chips have come a long way since then, culminating in the Tensor ...

The Google Tensor G5 has been announced, and the company claims that it brings the biggest leap in performance yet, as far as Tensor chips are concerned. This is the first TSMC-made Tensor chip with a ...

The Pixel 10 series is powered by the Tensor G5, Google’s biggest upgrade to its custom silicon. Google touts deeper customization, starting with how it’s manufactured by TSMC on the latest 3nm ...

A processing unit in an NVIDIA GPU that accelerates AI neural network processing and high-performance computing (HPC). There are typically from 300 to 600 Tensor cores in a GPU, and they compute ...