Note
The master branch is now used to work on the upcoming Intel MKL-DNN v1.0 release with changes that are incompatible with v0.x. The changes are described in the following RFC.
For a limited time, the team will maintain 0.x branch, backporting fixes and some of the features from the mainline.
Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an open-source performance library for deep-learning applications. The library accelerates deep-learning applications and frameworks on Intel(R) architecture and Intel(R) Processor Graphics Architecture. Intel MKL-DNN contains vectorized and threaded building blocks that you can use to implement deep neural networks (DNN) with C and C++ interfaces.
Note Intel(R) MKL-DNN is distinct from Intel(R) MKL, which is general math performance library.
This release contains performance-critical functions that improve performance of the following deep learning topologies and variations of these:
| Application | Example topology |
|---|---|
| Image recognition | AlexNet, VGG, GoogleNet, ResNet, MobileNet |
| Image segmentation | FCN, SegNet, MaskRCNN, U-Net |
| Volumetric segmentation | 3D-Unet |
| Object detection | SSD, Faster R-CNN, Yolo |
| Neural machine translation | GNMT |
| Speech recognition | DeepSpeech |
| Adversarial networks | DCGAN, 3DGAN |
| Reinforcement learning | A3C |
| Text-to-speech | WaveNet |
Intel MKL-DNN is used in the following software products (please let us know if you are using the library inside your appication so we can add to the list):
- Caffe* Optimized for Intel Architecture
- Chainer*
- DeepBench
- PaddlePaddle*
- PyTorch*
- Tensorflow*
- Microsoft* Cognitive Toolkit (CNTK)
- Apache* MXNet
- OpenVINO(TM) toolkit
- Intel Nervana Graph
- Menoh*
- DeepLearning4J*
- BigDL
Intel MKL-DNN is licensed under Apache License Version 2.0. This software includes the following third-party components:
- Xbyak distributed under 3-clause BSD licence
- gtest distributed under 3-clause BSD license
- ittnotify distributed under 3-clause BSD license
- Developer guide explains programming model, supported functionality, details of primitives implementations and includes annotated examples.
- API reference provides comprehensive reference of the library API.
Please submit your questions, feature requests, and bug reports on the GitHub issues page.
WARNING The following functionality has preview status and might change without prior notification in future releases.
- Threading Building Blocks (TBB) support
- Intel(R) Processor Graphics support
We welcome community contributions to Intel MKL-DNN. If you have an idea on how to improve the library:
- Share your proposal via GitHub issues.
- Ensure that you can build the product and run all the examples with your patch.
- In the case of a larger feature, create a test.
- Submit a pull request.
We will review your contribution and, if any additional fixes or modifications are necessary, may provide feedback to guide you. When accepted, your pull request will be merged to the repository.
Intel MKL-DNN supports Intel 64 architecture and compatible architectures. The library is optimized for the systems based on
- Intel Atom(R) processor with Intel SSE4.1 support
- 4th, 5th, 6th, 7th, and 8th generation Intel(R) Core(TM) processor
- Intel(R) Xeon(R) processor E3, E5, and E7 family (formerly Sandy Bridge, Ivy Bridge, Haswell, and Broadwell)
- Intel(R) Xeon(R) Scalable processors (formerly Skylake and Cascade Lake)
- Intel(R) Xeon Phi(TM) processors (formerly Knights Landing and Knights Mill)
and compatible processors.
Intel MKL-DNN supports Intel(R) Processor Graphics. The library is optimized for the systems based on
- Intel(R) Iris(R) Pro Graphics.
The software dependencies are:
- CMake 2.8.11 or later
- Doxygen 1.8.5 or later
- C++ compiler with C++11 standard support
- Optional dependencies:
- GNU* OpenMP*, LLVM OpenMP, or Intel OpenMP
- Threading Building Blocks (TBB) 2017 or later
- Intel MKL 2017 Update 1 or Intel MKL small libraries
The additional software dependencies for Intel(R) Processor Graphics support:
- OpenCL* runtime library (OpenCL* version 1.2 or later)
- OpenCL* driver (with kernel language support for OpenCL* C 2.0 or later) with Intel(R) subgroups extension support
Note Building Intel MKL-DNN with optional dependencies may introduce additional runtime dependencies for the library. For details, refer to the corresponding software system requirements.
The software was validated on RedHat* Enterprise Linux 7 with
- GNU Compiler Collection 4.8, 5.4, 6.1, 7.2, and 8.1
- Clang* 3.8.0
- Intel C/C++ Compiler 17.0, 18.0, and 19.0
on Windows Server* 2012 R2 with
- Microsoft Visual C++ 14.0 (Visual Studio 2015 Update 3)
- Intel C/C++ Compiler 17.0 and 19.0
on macOS* 10.13 (High Sierra) with
- Apple LLVM version 9.2 (XCode 9.2)
- Intel C/C++ Compiler 18.0 and 19.0
Intel(R) Processor Graphics support was validated on Ubuntu* 18.04 with
- GNU Compiler Collection 5.4 and 8.1
- Clang* 3.8.1
- Intel C/C++ Compiler 19.0
- Intel(R) SDK for OpenCL* applications 2019 Update 3
- Intel(R) Graphics Compute Runtime for OpenCL* 19.15.12831
on Windows Server* 2019 with
- Microsoft Visual C++ 14.0 (Visual Studio 2015 Update 3)
- Intel C/C++ Compiler 19.0
- Intel(R) SDK for OpenCL* applications 2019 Update 3
- Intel(R) Graphics - Windows* 10 DCH Drivers 26.20.100.6709
The implementation uses OpenMP 4.0 SIMD extensions. We recommend using the Intel C++ Compiler for the best performance results.