ZenDNN (Zen Deep Neural Network) Library accelerates deep learning inference applications on AMD CPUs. This library, which includes APIs for basic neural network building blocks optimized for AMD CPUs, targets deep learning application and framework developers with the goal of improving inference performance on AMD CPUs.
ZenDNN v3.2 Highlights Downloads Documentation
- Enabled, tuned, and optimized for AMD 2nd and 3rd Gen EPYC processors
- Integrated with TensorFlow v2.7, PyTorch v1.9.0, and ONNXRT v1.8.0
- Features highly optimized primitives for AMD CPUs, targeting a variety of workloads, including computer vision, natural language processing, and recommender systems
- Tested with a variety of neural network models across three major workload types:
- Computer Vision: AlexNet, InceptionV3, InceptionV4, GoogLeNet, ResNet50, ResNet152, VGG16, and VGG19
- Natural Language Processing: BERT
- Recommender Systems: DLRM, Wide & Deep
- Supported on Ubuntu 18.04, Ubuntu 20.04, RHEL 8.3, and CentOS 8.3
ZenDNN will be available shortly on GitHub (https://github.com/amd/ZenDNN). ZenDNN library is intended to be used in conjunction with the frameworks mentioned above and cannot be used independently.
Previous Release
ZenDNN v3.1 Highlights
- Enabled, tuned, and optimized for AMD 2nd and 3rd Gen EPYC processors
- Integrated with TensorFlow v2.5
- Features highly optimized primitives for AMD CPUs, including: Convolution, MatMul, Elementwise (Binary), BatchNorm, and ReLU
- Tested with a variety of neural network models across three major workload types:
- Computer Vision: AlexNet, InceptionV3, InceptionV4, GoogLeNet, ResNet50, ResNet152, VGG16, and VGG19
- Natural Language Processing: BERT
- Recommender Systems: DLRM, Wide & Deep
- Supported on Ubuntu 18.04, Ubuntu 19.10, Ubuntu 20.04, RHEL 8.3, and CentOS 8.3
-
INT8 models supported and tested with RESNET50 variants
Resources and Technical Support
Resources
Documentation
TensorFlow + ZenDNN User Guide
ZenDNN support matrix lists the details of ZenDNN releases and other relevant artifacts.
Technical Support
Refer Technical Support for support options.
Downloads
TensorFlow and PyTorch are available with different Python versions. ONNXRT is available with Python v3.8 and docker.
For previous releases of ZenDNN, please refer here.