The term "flow" refers to this movement of data through the various stages of model training or inference. Graphs: One of the reasons for TensorFlow’s popularity is its graph-based architecture.

Whereas the existing TensorFlow runtime was initially built for graph execution and training workloads, the new runtime will make eager execution and inference first-class citizens. Graph ...

You’ll see terms like “sea-of-MACs,” “systolic array,” “dataflow architecture,” “graph ... and inference rate.” Fig. 1: A block diagram for devices using Syntiant’s second-generation core. The ...

Since 2012, engineers and data scientists at the media giant Disney have been building what the company calls the Content Genome, a knowledge graph ... in inference performance from TensorFlow ...

Google has revealed new benchmark results for its custom TensorFlow processing unit, or TPU. In inference workloads, the company's ASIC positively smokes hardware from Intel, Nvidia. Share on ...

In 2020, Rajat left Google to start Inference.io, to fix these problems with analytics by leveraging AI – the area he focused on with TensorFlow ... the client to save time reviewing multiple graphs ...

TensorFlow can be used for a type of artificial intelligence called deep learning, which involves training artificial neural networks on lots of data and then getting them to make inferences about ...

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