https://github.com/inventwithdean/cuda_mlp

Implementation of a simple Multilayer Perceptron in pure CUDA
https://github.com/inventwithdean/cuda_mlp

cuda cuda-programming deep-learning neural-networks

Last synced: 4 days ago
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Implementation of a simple Multilayer Perceptron in pure CUDA

• Host: GitHub
• URL: https://github.com/inventwithdean/cuda_mlp
• Owner: inventwithdean
• Created: 2024-08-04T06:29:58.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-08-07T18:16:06.000Z (6 months ago)
• Last Synced: 2025-02-05T05:44:16.183Z (4 days ago)
• Topics: cuda, cuda-programming, deep-learning, neural-networks
• Language: Cuda
• Homepage:
• Size: 63.5 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
This repository contains a pure CUDA C++ implementation of a Multilayer Perceptron (MLP) neural network with a Linear Regression Dataset with Apache 2 license on which the network attained a loss of 0.07. By building from the ground up, we gain a deep understanding of the inner workings of neural networks and the performance benefits of GPU acceleration.

## Disclaimer: 

* This project is for educational purposes and may not be optimized for production use.

## Features:

* Pure CUDA C++ implementation: No external libraries or frameworks.

* GPU acceleration: Leverage the power of GPUs for high performance.

* Modular design: Clear separation of concerns for maintainability.

  

## Getting Started:

* Clone the repository:

Bash

`git clone https://github.com/inventwithdean/CUDA_MLP.git`

* Set up CUDA environment: Ensure you have a CUDA-capable GPU and the necessary CUDA toolkit installed.

* Compile the code: Use a CUDA-compatible compiler to build the project.

* Run the executable: Execute the generated binary to run the MLP.

## Structure:

* include: Header files for classes and functions along with some kernels for MSE and ReLUs.

* kernels: CUDA kernels for matrix related calculations

* main.cu: Main file where dataset is loaded and Optimization loop happens

* dataset.cu: A very simple and lightweight csv Dataset Loader class

## Future Improvements:

* Implementing Softmax and Convolutional layers.

* Add optimization techniques like momentum.

* Explore different activation functions and network architectures.

* Improve performance through kernel tuning and optimization.

## Contributing:

Feel free to contribute to this project by:

* Submitting bug reports

* Suggesting new features

* Improving the code

* Writing documentation

  

Let's dive into the world of deep learning together!

## License:

MIT