🎯 FPGA Programming & Deep Learning Implementation

Hardware-Accelerated Deep Learning on FPGA

📚 Table of Contents

• Overview
• Projects
• Architecture
• Performance
• Getting Started
• Contributing
• License

🌟 Overview

Advanced FPGA implementations of cutting-edge deep learning models, optimized for high performance and energy efficiency.

🏗️ Architecture

System Overview

graph TD
    A[Host CPU] -->|Configuration| B[FPGA]
    B -->|Results| A
    B --> C[Memory Controller]
    C --> D[DDR Memory]
    B --> E[Neural Engine]
    E --> F[Systolic Array]
    E --> G[Activation Unit]

Loading

Memory Hierarchy

L1 Cache (On-Chip)  : 64KB
L2 Cache (On-Chip)  : 256KB
External DDR        : 4GB

🛠️ Getting Started

Prerequisites

# Required Software
- Xilinx Vivado 2023.1
- Python 3.8+
- TensorFlow 2.x

🤝 Contributing

We welcome contributions! See our Contributing Guidelines.

Development Workflow

1. Fork repository
2. Create feature branch
3. Implement changes
4. Submit pull request
5. Code review
6. Merge

📄 License

Apache License 2.0 - LICENSE