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Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software. [2] Google began using TPUs internally in 2015, and in 2018 made them available for third-party use, both as part of its cloud infrastructure and by ...
TPU or tpu may refer to: Science and technology. Tensor Processing Unit, a custom ASIC built by Google, tailored for their TensorFlow platform;
Groq was founded in 2016 by a group of former Google engineers, led by Jonathan Ross, one of the designers of the Tensor Processing Unit (TPU), an AI accelerator ASIC, and Douglas Wightman, an entrepreneur and former engineer at Google X (known as X Development), who served as the company’s first CEO.
In May 2016, Google announced its Tensor processing unit (TPU), an application-specific integrated circuit (ASIC, a hardware chip) built specifically for machine learning and tailored for TensorFlow. A TPU is a programmable AI accelerator designed to provide high throughput of low-precision arithmetic (e.g., 8-bit ), and oriented toward using ...
The report said Google's deliberations follow a standoff between the company and Broadcom over the price of the TPU chips. Google has also been working to replace Broadcom with Marvell Technology ...
Google Tensor is a series of ARM64-based system-on-chip (SoC) processors designed by Google for its Pixel devices. It was originally conceptualized in 2016, following the introduction of the first Pixel smartphone , though actual developmental work did not enter full swing until 2020.
A single source for discussing TPU devices seems like a good idea as this market grows. However, I am not sure if this is the place. Right now this is focused on the Google TPU products, and would require a distinct re-write to make it generic. I am not opposed to a doing so, but I am not sure if there is a consensus that TPU has been genericized.
11.5×10 15: Google TPU pod containing 64 second-generation TPUs, May 2017 [9] 17.17×10 15: IBM Sequoia's LINPACK performance, June 2013 [10] 20×10 15: roughly the hardware-equivalent of the human brain according to Ray Kurzweil. Published in his 1999 book: The Age of Spiritual Machines: When Computers Exceed Human Intelligence [11]