The inception of Google’s effort to build its own AI chips is quite well known by now but in the interests of review, we’ll note that as early 2013 the company envisioned machine learning could consume the majority of its compute time. …
Carey Kloss has been intimately involved with the rise of AI hardware over the last several years, most notably with his work building the first Nervana compute engine, which Intel captured and is rolling into two separate products: one chip for training, another for inference. …
Training deep neural networks is one of the more computationally intensive applications running in datacenters today. …
This week we have heard much about the inference side of the deep learning workload, with a range of startups emerging at the AI Hardware Summit. …
If there is anything the hyperscalers have taught us, it is the value of homogeneity and scale in an enterprise. …
Google did its best to impress this week at its annual IO conference. …
Google laid down its path forward in the machine learning and cloud computing arenas when it first unveiled plans for its tensor processing unit (TPU), an accelerator designed by the hyperscaler to speeding up machine learning workloads that are programmed using its TensorFlow framework. …
As we previously reported, Google unveiled its second-generation TensorFlow Processing Unit (TPU2) at Google I/O last week. …
Google created quite a stir when it released architectural details and performance metrics for its homegrown Tensor Processing Unit (TPU) accelerator for machine learning algorithms last week. …
Four years ago, Google started to see the real potential for deploying neural networks to support a large number of new services. …
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