AMD Zen Deep Neural Network (ZenDNN)

ZenDNN library, which includes APIs for basic neural network building blocks optimized for AMD CPU architecture, targets deep learning application and framework developers with the goal of improving deep learning inference performance on AMD CPUs.

ZenDNN v3.3 Highlights                      Downloads   Documentation

• Enabled, tuned, and optimized for inference on AMD 3^rd Generation EPYC^TM processors
• Integrated with TensorFlow v2.9 and PyTorch v1.11.0
• Features highly optimized primitives for AMD CPUs, targeting a variety of workloads, including computer vision, natural language processing, and recommender systems
• This release of ZenDNN works with AMD optimized models from Unified Inference Frontend (UIF), which are intended to provide significant performance improvement (in the range of 2-5x). More than 50 Xilinx optimized models are available in this release. For more information on these models, refer to the “UIF Documentation” section.
• Tested with a variety of neural network models across three major workload types:
  • Computer Vision: AlexNet, InceptionV3, InceptionV4, GoogLeNet, ResNet50, ResNet152, VGG16, VGG19
  • Natural Language Processing: BERT
  • Recommender Systems: DLRM, Wide & Deep
• Supported on Ubuntu 18.04, Ubuntu 20.04, RHEL 8.3, SLES15 SP3

Resources and Technical Support

Resources

Documentation

ZenDNN User Guide

TensorFlow + ZenDNN User Guide

PyTorch + ZenDNN User Guide

ZenDNN support matrix lists the details of ZenDNN releases and other relevant artifacts.

UIF Documentation

UIF documentation is available on GitHub (https://github.com/amd/UIF).

This GitHub page describes how to incorporate the AMD optimized models into your workflow and provides guidance on the possible performance uplift.

Installation Tutorials

Technical Support

For support options, refer to Technical Support.

Downloads

TensorFlow and PyTorch are available with different Python versions.

For previous ZenDNN releases, refer to ZenDNN Archive.