How to Accelerate TensorFlow on Intel Hardware

When deploying deep learning models, inference speed is usually measured in terms of latency or throughput, depending on your application's requirements. Latency is how quickly you can get an answer, whereas throughput is how much data the model can process in a given amount of time. Both use cases benefit from accelerating the inference operations of the deep learning framework running on the target hardware. Engineers from Intel and Google have collaborated to optimize TensorFlow* running on Intel hardware. This work is part of the Intel oneAPI Deep Neural Network Library (oneDNN) and available to use as part of standard TensorFlow.