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N2D2 is a open source CAD framework for Deep Neural Network simulation and full DNN-based applications building.
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.appveyor Force OpenCV installation with AppVeyor Jul 18, 2017
.travis Fetch keys for Travis Sep 4, 2019
cmake/modules Use a precompiled header to speed-up compilation. Jan 31, 2019
docs Updated doc Mar 9, 2020
exec Added DeepNet::fusePadding() May 5, 2020
export Add ElemWise Layer and Scaling Layer (INT scaling) to export C module May 25, 2020
include Fixed MSVC error May 26, 2020
manual Updated doc Oct 15, 2019
models Added MobileNet_v2_ONNX files + PyTorch convertion script May 5, 2020
python Added basic (and very incomplete) Python binding Oct 10, 2019
tests Added CompressionNoiseTransformation Apr 2, 2020
tools Fix bias import issue in import_tensorflow May 26, 2020
.clang-format Release ready Jan 20, 2017
.gitignore Added basic functionality in CEnvironment to feed analog input to spi… Feb 1, 2019
.travis.yml Added key for Travis Sep 4, 2019
CMakeLists.txt Remove the -Wno-unused-variable compile option from the CMakeLists.txt. Dec 10, 2019 Create Jul 18, 2017 Release ready Jan 20, 2017
Dockerfile Updated Dockerfile Nov 23, 2017
LICENSE Added LICENSE file Jan 6, 2017
Makefile Fixed memory leak Mar 19, 2020 Improved docs Oct 14, 2019
appveyor.yml Add -m option to Appveyor CI to parallelize the build process. Nov 27, 2018
doxygen.cfg Release ready Jan 20, 2017


Docs Linux CPU
≥ GCC 4.4.7
Linux GPU
≥ CUDA 6.5 + CuDNN 1.0
Windows CPU
≥ Visual Studio 2015.2
Windows GPU
≥ CUDA 8.0 + CuDNN 5.1
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N2D2 (for 'Neural Network Design & Deployment') is a open source CAD framework for Deep Neural Network (DNN) simulation and full DNN-based applications building. It is developped by the CEA LIST along with industrial and academic partners and is open to community contributors. The only mandatory dependencies for N2D2 are OpenCV (≥ 2.0.0) and Gnuplot. The NVIDIA CUDA and CuDNN libraries are required to enable GPU-acceleration.

If you did like to contribute to N2D2, please make sure to review the contribution guidelines.


To compile and use N2D2, please refer to the manual, which contains the following resources:

  • General presentation of the framework;
  • How to compile N2D2 and perform simulations;
  • How to write neural network models;
  • Tutorials.

The PDF manual will soon be superseded by the online documentation. In particular, the N2D2 Python API documentation will only be available here.

The N2D2 executables and application examples are located in the exec directory.

Obtain N2D2 with Docker: docker pull cealist/n2d2


N2D2 is released under the CeCILL-C license, a free software license adapted to both international and French legal matters that is fully compatible with the FSF's GNU/LGPL license.

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