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Last Commit
Sep. 22, 2018
Aug. 19, 2018


This is a small pure C shared library for arbitrarily deep neural networks. It is my first attempt to write a scientific project in C but the speed is already outstanding. I made this library in order to assist me with my doctoral research.

It is CPU (OpenBLAS) and GPU (CLBlast) friendly (soon with support for cuBLAS if I get my hands on an NVIDIA GPU). I have plans to extend it for CNNs and RNNs.


The feedforward procedure does not have a hardcoded depth (it can have as many layers as you want).

Getting Started

The library is created with Linux machines in mind but OSX users should not have a problem if they have gcc installed. I will try to compile the library with Visual Studio and get back to you on how to do it.

In order to get libartificial you have to do the following (assuming working installation of git)

git clone
cd libartificial
rm -rf .git


In order to compile the library for CPU you need to install OpenBLAS. In order to compile the library for GPU you need to install CLBlast.

Specifics for CLBlast

It is recommended to do the optimizations proposed by the author. The library assumes that CLBlast is in the libartificial folder under the name "clblast". You do the following:

git clone clblast
cd clblast && mkdir build && cd build
cmake .. && make
cd ../../

You should also take note that the library uses doubles and not all GPUs support double arithmetic in OpenCL.


In order to compile the library do the following (assuming you continue from where we left off)

  • For CPU
make cpu
  • For GPU
make gpu


For the time being I have four examples which you can find in the "examples" folder:

  • MLP regression with CPU:
make test1
  • MLP regression with GPU
make test2
  • Autoencoder (CPU):
make test3
  • CNN (only im2col at the moment):
make test4


This part is being written at the moment


If you like my work and/or you want to use it for your own projects or want me to create a custom recipe for you, I would gladly accept your donations at:


ETH: 0xf09fce52f7ecd940cae2826deae151b6495354f6


Copyright (c) Jim Karoukis. This project is licensed under the MIT License - see the LICENSE file for details.