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  2. Tensor Processing Unit - Wikipedia

    en.wikipedia.org/wiki/Tensor_Processing_Unit

    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 ...

  3. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    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 ...

  4. Broadcom falls on report Google discussed dropping firm as AI ...

    www.aol.com/news/google-discussed-dropping-broad...

    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 ...

  5. Google Brain - Wikipedia

    en.wikipedia.org/wiki/Google_Brain

    Google Brain was a deep learning artificial intelligence research team that served as the sole AI branch of Google before being incorporated under the newer umbrella ...

  6. Inception (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Inception_(deep_learning...

    Inception [1] is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1).). The series was historically important as an early CNN that separates the stem (data ingest), body (data processing), and head (prediction), an architectural design that persists in all modern

  7. XLNet - Wikipedia

    en.wikipedia.org/wiki/XLNet

    It was trained on 512 TPU v3 chips, for 5.5 days. At the end of training, it still under-fitted the data, meaning it could have achieved lower loss with more training. It took 0.5 million steps with an Adam optimizer , linear learning rate decay, and a batch size of 8192.

  8. Google’s AI prophet fast tracks singularity prediction - AOL

    www.aol.com/news/google-ai-prophet-fast-tracks...

    Bill Gates calls Ray Kurzweil ‘the best person I know at predicting the future of artificial intelligence’

  9. Google launches Trillium chip, improving AI data center ... - AOL

    www.aol.com/news/google-launches-trillium-chip...

    The sixth-generation Trillium chip will achieve 4.7 times better computing performance compared with the TPU v5e, according to Google, a chip designed to power the tech that generates text and ...