Open Source TensorFlow Powered by Optimizations from Intel

  • Accelerate AI performance with Intel® oneAPI Deep Neural Network Library (oneDNN) features such as graph optimizations and memory pool allocation.
  • Automatically use Intel® Deep Learning Boost instruction set features to parallelize and accelerate AI workloads.
  • Reduce inference latency for models deployed using TensorFlow Serving.
  • Starting with TensorFlow 2.9, take advantage of oneDNN optimizations automatically.
  • Enable optimizations by setting the environment variable TF_ENABLE_ONEDNN_OPTS=1 in TensorFlow 2.5 through 2.8.

Intel® Extension for TensorFlow* 

  • Plug into TensorFlow 2.10 or later to accelerate training and inference on Intel GPUs with no code changes.
  • Automatically mix precision using bfloat16 or float16 data types to reduce memory footprint and improve performance.
  • Use TensorFloat-32 (TF32) math mode on Intel GPU hardware.
  • Optimize CPU performance settings for latency or throughput using an autotuned CPU launcher.
  • Perform more aggressive fusion through the oneDNN Graph API.

Optimized Deployment with OpenVINO™ Toolkit

  • Import your TensorFlow model into OpenVINO™ Runtime and use the Neural Networks Compression Framework (NNCF) to compress model size and increase inference speed.
  • Deploy with OpenVINO model server for optimized inference, accessed via the same API as TensorFlow Serving.
  • Target a mix of Intel CPUs, GPUs (integrated or discrete), NPUs, or FPGAs.
  • Deploy on-premise and on-device, in the browser, or in the cloud.