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jaewoosong / pocketnn

The official, proof-of-concept C++ implementation of PocketNN.
MIT License
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PocketNN

This is an official, proof-of-concept C++ implementation of the paper PocketNN: Integer-only Training and Inference of Neural Networks via Direct Feedback Alignment and Pocket Activations in Pure C++. The paper was accepted in TinyML 2022 Research Symposium as a full paper.

Take a look!

Just run the Main.cpp file to see training and testing a PocketNN network with the MNIST dataset! Other sample usages can be found in pktnn_examples.cpp file.

demo screen capture animation

Presentation video

Please click the image below to watch a youtube video which was recorded at tinyML 2022.

Youtube link of TinyML 2022: PocketNN Presentation

A presentation video recorded at tinyML 2022.

Citing PocketNN (BibTeX)

@article{song2022pocketnn,
  title={PocketNN: Integer-only Training and Inference of Neural Networks via Direct Feedback Alignment and Pocket Activations in Pure C++},
  author={Song, Jaewoo and Lin, Fangzhen},
  journal={arXiv preprint arXiv:2201.02863},
  year={2022}
}

How to Build

There are two directories, PocketNN_linux and PocketNN_windows, because of slight differences between C++ development environment of Linux and Windows. The algorithm is identical.

Linux

PocketNN code for Linux is in the PocketNN_linux directory. Run the following commands to build on Linux.

cd PocketNN_linux
mkdir build
cd build
cmake ..
make

The libcurl library is required in order to download sample datasets from the internet. Please note that libcurl is just for downloading files from the internet, not related to PocketNN algorithm at all. If libcurl is not on your system, you can install it by the following commands:

# Ubuntu
sudo apt-get install libcurl4-openssl-dev

# Centos
sudo yum install libcurl-devel

# macOS
brew install curl

Then you can run PocketNN in the build directory to see the demo.

$ ./PocketNN
Loading dataset/mnist/train-labels.idx1-ubyte
Magic number: 2049, Total items: 60000
Loading dataset/mnist/train-images.idx3-ubyte
Magic number: 2051, Total items: 60000, Rows: 28, Cols: 28
Loading dataset/mnist/t10k-labels.idx1-ubyte
Magic number: 2049, Total items: 10000
Loading dataset/mnist/t10k-images.idx3-ubyte
Magic number: 2051, Total items: 10000, Rows: 28, Cols: 28
Loaded train images,60000,Loaded test images,10000
Initial training numCorrect,5923
Initial test numCorrect,980
----- NOW START -----
(CSV format)
Learning Rate Inverse,1000,numTrainSamples,60000,miniBatchSize,20
Training
Epoch,SumLoss,NumCorrect,Accuracy
Initialized DFA!
Initialized DFA!
1,1087826,54475,0.907917
2,623752,57127,0.952117
3,518970,57708,0.9618
Final training numCorrect,57866

Test
Epoch,NumCorrect
1,9495
2,9575
3,9593
Final test numCorrect,9593
Final test accuracy,0.9593
Final learning rate inverse,1000
$

Windows

The PocketNN_windows directory contains PocketNN code for Windows. I used Visual Studio 2019 to write the code. Visual Studio solution file is included in the repository to help importing the project.

License

PocketNN uses the MIT License. For details, please see the LICENSE file.

Sample datasets

Two sample datasets are copied from their original website.