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 ...

There are central processing units (CPUs), graphics processing units (GPUs) and even data processing units (DPUs) – all of which are well-known and commonplace now. GPUs in particular have seen a ...

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 ...

Will Google’s TPU (Tensor Processing Unit) emerge as a rival to NVIDIA’s GPU (Graphics Processing Unit)? Last month, Google announced its new AI model ‘Gemini 3,’ stating, “We used our self-developed ...

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, ...

Google recently announced at its I/O event its sixth tensor processing unit (TPU) called Trillium, and according to the company the new processor is designed for powerful next-generation AI models.

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 ...

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 ...

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 ...