# Writing a Convolutional Neural Network from Scratch with CUDA: Deep Dive into GPU Parallel Computing and Deep Learning Underlying Implementations

> A convolutional neural network project implemented entirely from scratch using CUDA without relying on any deep learning frameworks, demonstrating core principles of GPU parallel computing and performance optimization techniques

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-13T09:26:49.000Z
- 最近活动: 2026-05-13T09:29:01.227Z
- 热度: 160.0
- 关键词: CUDA, 卷积神经网络, GPU并行计算, 深度学习底层, 性能优化, 手写神经网络, MNIST, 反向传播
- 页面链接: https://www.zingnex.cn/en/forum/thread/cuda-gpu
- Canonical: https://www.zingnex.cn/forum/thread/cuda-gpu
- Markdown 来源: floors_fallback

---

## Introduction: Writing a CNN from Scratch with CUDA, Deep Dive into GPU Parallelism and Deep Learning Underlying Layers

This article introduces the open-source project **CUDA-CNN-from-scratch**, created by developer claudiocamolese. It implements a convolutional neural network entirely from scratch using CUDA without relying on any deep learning frameworks. The project demonstrates core principles of GPU parallel computing and performance optimization techniques, supports MNIST and Fashion-MNIST datasets, and achieves a test accuracy of 98.08% after 5 training epochs. It is a practical resource for understanding the underlying implementation of deep learning.

## Project Background and Significance

The high abstraction of deep learning frameworks (such as PyTorch and TensorFlow) often causes developers to overlook the essence of underlying computations. When calling `model.forward()`, there is a lack of intuitive understanding of issues like how gradients flow on the GPU and how memory is managed. This project aims to answer these questions by implementing a CNN from scratch using CUDA, serving as a practical course on GPU parallel computing and deep learning underlying principles.

## Project Architecture and Network Design

The project implements a classic LeNet-style CNN:
- First convolution layer: 1→16 channels, 3×3 kernel + ReLU
- Second convolution layer:16→32 channels,3×3 kernel + ReLU
- 2×2 max pooling layer
- Flatten operation
- Fully connected layer
- Softmax layer
It supports MNIST and Fashion-MNIST classification tasks, achieving a test accuracy of 98.08% after 5 training epochs.

## Core Implementation: Forward and Backward Propagation

All computation steps of the neural network are implemented manually: forward propagation includes convolution, activation, pooling, and fully connected layers; backward propagation calculates gradients for each layer and backpropagates to update parameters. The Stochastic Gradient Descent (SGD) optimizer is used, and all computations are performed on the GPU, helping to understand core concepts such as tensor operations, memory access patterns, and thread block partitioning.

## Performance Optimization Techniques

The project explores various GPU optimization techniques:
1. **Kernel Fusion**: Fuse convolution and ReLU to reduce global memory read/write operations
2. **Shared Memory Tiling**: Use shared memory for matrix multiplication in fully connected layers to reduce repeated access to global memory
3. **Warp-Level Reduction**: Use the `__shfl_down_sync` instruction in the backward propagation convolution kernel to improve gradient calculation efficiency
4. **Triple Buffering and CUDA Streams**: Overlap asynchronous transfer and computation to hide latency
5. **Pinned Memory**: Enable `cudaMemcpyAsync` for asynchronous transfer to improve data movement efficiency

## Performance Comparison with PyTorch

The project conducts experiments comparing with PyTorch, analyzing the impact of parameters like `num_workers` (number of data loading subprocesses) and `pin_memory` (pinned memory). Although PyTorch's underlying layers are highly optimized, the handwritten CUDA version is still competitive after full optimization, and it provides full control over the computation process, allowing for extreme optimization for specific scenarios.

## Practical Value and Learning Significance

For learners of deep learning underlying layers, the project provides a rare resource:
- Understand how CUDA kernels process convolutions in parallel
- Master efficient GPU implementation of the chain rule in backward propagation
- Comprehend the impact of memory management strategies on performance
- Learn the effects and trade-offs of optimization techniques
The code structure is clear, includes complete CMake configuration, supports compilation in multi-GPU environments, and is suitable for academic research, teaching, or getting started with high-performance computing.

## Conclusion

CUDA-CNN-from-scratch proves that without PyTorch/TensorFlow, a fully functional and high-performance deep learning system can be built using CUDA C++. The process of building from scratch deepens understanding of algorithm principles and cultivates engineering problem-solving abilities. In today's era of mature AI frameworks, understanding underlying implementations is a valuable investment in technical depth.