Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an open source performance library for Deep Learning (DL) applications intended for acceleration of DL frameworks on Intel(R) architecture. Intel(R) MKL-DNN includes highly vectorized and threaded building blocks for implementation of convolutional neural networks (CNN) with C and C++ interfaces. We created this project to enable the DL community to innovate on Intel(R) processors.
Intel MKL-DNN includes functionality similar to Intel(R) Math Kernel Library (Intel(R) MKL) 2017, but is not API compatible. We are investigating how to unify the APIs in future Intel MKL releases.
This release is a technical preview with functionality necessary to accelerate bleeding edge image recognition topologies, including Cifar*, AlexNet*, VGG*, GoogleNet* and ResNet*.
Intel MKL-DNN is licensed under Apache License Version 2.0.
The latest Intel MKL-DNN documentation is at GitHub pages.
Please report issues and suggestions via GitHub issues or start a topic on Intel MKL forum.
We welcome community contributions to Intel MKL-DNN. If you have an idea how to improve the library:
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Share your proposal via GitHub issues.
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Ensure you can build the product and run all the examples with your patch
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In the case of a larger feature, create a test
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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 into our internal and GitHub repositories.
Intel MKL-DNN supports Intel(R) 64 architecture processors and is optimized for
- Intel Atom(R) processor with Intel(R) SSE4.1 support
- 4th, 5th, 6th and 7th generation Intel(R) Core processor
- Intel(R) Xeon(R) processor E5 v3 family (code named Haswell)
- Intel(R) Xeon(R) processor E5 v4 family (code named Broadwell)
- Intel(R) Xeon(R) Platinum processor family (code name Skylake)
- Intel(R) Xeon Phi(TM) product family x200 (code named Knights Landing)
- Future Intel(R) Xeon Phi(TM) processor (code named Knights Mill)
The software dependencies are:
The software was validated on RedHat* Enterprise Linux 7 with
- GNU* Compiler Collection 4.8
- GNU* Compiler Collection 6.1
- Clang* 3.8.0
- Intel(R) C/C++ Compiler 16.0 or later
The implementation uses OpenMP* 4.0 SIMD extensions. We recommend using Intel(R) Compiler for the best performance results.
Download Intel MKL-DNN source code or clone the repository to your system
git clone https://github.com/01org/mkl-dnn.git
Satisfy all hardware and software dependencies and ensure that the versions are correct before installing. Intel MKL-DNN can take advantage of optimized matrix-matrix multiplication (GEMM) function from Intel MKL. The dynamic library with this functionality is included in the repository. If you choose to build Intel MKL-DNN with binary dependency download Intel MKL small libraries first using provided script
cd scripts && ./prepare_mkl.sh && cd ..
or download manually and unpack it to the external directory in the
repository root.
Intel MKL-DNN uses a CMake-based build system
mkdir -p build && cd build && cmake .. && make
Intel MKL-DNN includes unit tests implemented using the googletest framework. To validate your build, run:
make test
Documentation is provided inline and can be generated in HTML format with Doxygen:
make doc
Documentation will reside in build/reference/html folder.
Finally,
make install
will place the header files, libraries and documentation in /usr/local. To change
the installation path, use the option -DCMAKE_INSTALL_PREFIX=<prefix> when invoking CMake.