LLM-Aided FPGA Design for Signal Processing Applications

Overview

This project demonstrates a novel approach to FPGA design by leveraging Large Language Models (LLMs) to streamline the development process from algorithmic conception to hardware implementation, applied to a pulse detector similar to the example in the HDL Coder Self-Guided Tutorial by MathWorks. The design concepts and implementation details are visually illustrated in the PNG images within this repository.

Key Features

• MATLAB to HLS C++ Conversion: Automated translation of MATLAB DSP algorithms to HLS C++ using LLMs
• FIR IP Core Integration: Efficient techniques for incorporating optimized FIR IP cores within HLS designs
• Design Space Exploration (DSE): LLM-guided exploration of design alternatives and optimizations
• Automated Visualization: Python scripts for analyzing and visualizing resource usage and timing metrics
• Resource Efficiency: Comparable resource utilization to optimized MATLAB HDL Coder implementations

Project Structure

All project files are organized as follows:

pulseDetector/
├── MATLAB/               # MATLAB reference designs
├── HLS/                  # LLM-generated HLS C++ implementations
|   ├── origin/           # Origin version generated from MATLAB code
|   ├── resource_opt1/    # Merge to a single function
|   ├── resource_opt2/    # Use constant filter coefficient
|   ├── resource_opt3/    # Replace a complex data filter with three real data filters
│   └── resource_opt4/    # Optimized implementation with FIR IP core
└── Doc/                  # Implementation results and comparisons
    ├── *.png             # Visual diagrams of design concepts and workflows
    └── reportPrompt.md   # Prompt for generating Python code to visualize data

Getting Started

Prerequisites

• Vivado HLS (2022.2 or later recommended)
• MATLAB R2023a or later (for reference implementations)
• Python 3.8+ with matplotlib, pandas, and numpy

Installation

1. Clone the repository:

   git clone https://github.com/rockyco/pulseDetector.git
   cd pulseDetector

2. Set up Python environment:

   python -m pip install -r requirements.txt

3. Configure Vitis HLS paths:

   source /path/to/Vitis_HLS/2022.2/settings64.sh

Performance Analysis

Our LLM-aided approach achieves resource efficiency comparable to the MathWorks HDL Coder implementation:

Metric	LLM-Aided HLS Design	HDL Coder Design
DSP Utilization	194	194
LUT Utilization	745	407
FF Utilization	7405	4528
Maximum Frequency	304 MHz	265 MHz

Visual Design Documentation

Please refer to these images for a visual understanding of the project's methodology and outcomes:

• Introduction of pulse detector

• Design transformation workflow from MATLAB to HLS C++

• HLS implementation of pulse detector

• Resource utilization comparisons across implementation approaches

• Design space exploration results

Key Insights

1. Productivity Gains: LLM assistance significantly reduces development time compared to traditional approaches
2. Optimization Knowledge: LLMs can suggest HLS pragmas and optimizations based on design patterns
3. IP Core Integration: The hybrid approach of combining HLS with optimized IP cores provides the best balance of development speed and hardware efficiency
4. Design Exploration: LLMs facilitate rapid exploration of design alternatives that might otherwise be overlooked

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• MathWorks for the HDL Coder Self-Guided Tutorial
• The Xilinx Vitis HLS team for their comprehensive documentation
• The open-source community for contributions to FPGA design methodologies