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 will appear in TinyML 2022.

The very first run

Just run the main.cpp file to see training and testing a PocketNN network with the MNIST dataset! Other sample usages are written in pktnn_examples.cpp file.


I used Visual Studio 2019 to write this code. Visual Studio solution file is included in the repository to help importing the project.

Citing PocketNN

Citation information will be updated soon.


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

Sample datasets

Two sample datasets are copied from their original website.

  • MNIST dataset: MNIST dataset is from the MNIST website. The site says “Please refrain from accessing these files from automated scripts with high frequency. Make copies!” So I made the copies and put them in this repository.
  • Fashion-MNIST dataset: Fashion-MNIST dataset is from its github repository. It follows the MIT License which allows copy and distribution.


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