When Jim Keller talks about compute engines, you listen. And when Keller name drops a programming language and AI runtime environment, as he did in a recent interview with us, you do a little research and you also keep an eye out for developments. …
It seems like we have been talking about Google’s TPUv4 machine learning accelerators for a long time, and that is because we have been. …
TensorFlow, probably the most popular of the dozen or so deep learning frameworks, is typically used to develop neural networks on small or medium-sized clusters, and sometimes on just a single GPU-accelerated node. …
Despite some of the inherent complexities of using FPGAs for implementing deep neural networks, there is a strong efficiency case for using reprogrammable devices for both training and inference. …
The dark and mysterious art of artificial intelligence and machine learning is neither straightforward, or easy. …
These days, organizations are creating and storing massive amounts of data, and in theory this data can be used to drive business decisions through application development, particularly with new techniques such as machine learning. …
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. …
For developers, deep learning systems are becoming more interactive and complex. …
As we previously reported, Google unveiled its second-generation TensorFlow Processing Unit (TPU2) at Google I/O last week. …
While it is always best to have the right tool for the job, it is better still if a tool can be used by multiple jobs and therefore have its utilization be higher than it might otherwise be. …
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