Accelerate Deep Learning with Intel-Optimized TensorFlow | Intel® On | Intel Software

Опубликовано 8 июня 2022, 3:31

Learn how Intel and Google have collaborated to deliver TensorFlow optimizations such as quantization and op fusions. Penporn Koanantakook of Google joins Anavai Ramesh and Andres Rodriguez of Intel to discuss how to use TensorFlow with Intel Neural Compressor to automatically convert to int8 or Bfloat16 data types for improved performance with minimal accuracy loss. They also discuss the new PluggableDevice mechanism co-architected by Intel and Google to deliver a scalable way to add device support to TensorFlow.

Intel and Google have collaborated closely for over four years to deliver optimizations such as quantization and op fusions. Now these optimizations are available in standard TensorFlow packages.
Learn how to access these optimizations and how to use them from Anavai Ramesh and Andres Rodriguez of Intel, and Penporn Koanantakook of Google. This includes a live demo of Intel Neural Compressor automatically quantizing a network to improve performance by 4x with a 0.06% accuracy loss. Topics include:
• Low-precision (8-bit) inference optimization
• Bfloat16 overview and acceleration on 3rd Gen Intel® Xeon®
• TensorFlow Auto Mixed Precision
This session also provides an overview of the PluggableDevice mechanism in TensorFlow 2.5, co-architected by Intel and Google to deliver a scalable way for developers to add new device support as plug-in packages.
Finally, get a preview of performance gain results from pre-production next-generation Intel Xeon Scalable processors (code-name Sapphire Rapids).

TensorFlow: bit.ly/3tm6vSj

Intel Neural Compressor: intel.ly/3xtQ1dd

Getting Started with Bfloat16 mixed precision: intel.ly/3aCeF2d

Model Zoo for Intel Architecture: bit.ly/3mu3FqB

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Accelerate Deep Learning with Intel-Optimized TensorFlow | Intel® On | Intel Software