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Accelerate Deep Learning with Intel® Optimization for TensorFlow*

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Intel and Google* have been collaborating to deliver optimized implementations of some of the most compute-intensive TensorFlow operations. TensorFlow is an end-to-end open-source machine learning platform. Operations such as convolution filters require large matrix multiplications, which are extremely compute-intensive. Intel® oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform library that provides implementations of deep learning building blocks that use the same API for CPUs, GPUs, or both.

In this session:

  • Penporn Koanantakook of Google explains some of the optimizations that have been implemented, such as operation fusion, primitive caching, and vectorization of int8 and bfloat16 data types.
  • A live demo of Intel® Neural Compressor automatically quantizing a network to improve performance by 4x with a 0.06% accuracy loss.
  • An overview of the PluggableDevice mechanism in TensorFlow, co-architected by Intel and Google to deliver a scalable way for developers to add new device support as plug-in packages.

Note that this presentation was current as of TensorFlow 2.8. Starting with TensorFlow 2.9, the oneDNN optimizations are on by default, no longer requiring the TF_ENABLE_ONEDNN_OPTS=1 variable setting.

Get the Software

Accelerate end-to-end machine learning and data science pipelines with optimized deep learning frameworks and high-performing Python* libraries in Intel® oneAPI AI Analytics Toolkit.


About the Author
Technical marketing manager for Intel AI/ML product and solutions. Previous to Intel, I spent 7.5 years at MathWorks in technical marketing for the HDL product line, and 20 years at Cadence Design Systems in various technical and marketing roles for synthesis, simulation, and other verification technologies.