FPGA NN

A neural network built in Verilog for the DE1-SoC FPGA board for handwritten digit recognition.

AI & Machine LearningVerilogGPL-3.0

Abstract

FPGA NN is an open-source AI & Machine Learning project. A neural network built in Verilog for the DE1-SoC FPGA board for handwritten digit recognition. It is built using Verilog. The complete source code is publicly available on GitHub under the GNU General Public License v3.0, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

A neural network built in Verilog for the DE1-SoC FPGA board for handwritten digit recognition.

The neural network contains 4 layers of sizes 32, 32, 32, and 10.

It is designed with the MNIST database in mind; hence, images should be 28x28.

2. Objective

A neural network built in Verilog for the DE1-SoC FPGA board for handwritten digit recognition.

This project demonstrates how Verilog can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

Verilog

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • See the project README for exact requirements
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/FSq-Poplar/FPGA_NN.git
cd FPGA_NN

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by FSq-Poplar and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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