# AiEDA
**Repository Path**: oscc-project/AiEDA
## Basic Information
- **Project Name**: AiEDA
- **Description**: 一个用于AI for EDA的python库，主要功能包括：跑芯片流程产生数据、提取标签数据和特征、搭建神经网络模型并训练、模型接入芯片设计流程验证
- **Primary Language**: Unknown
- **License**: MulanPSL-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-09-03
- **Last Updated**: 2025-09-11
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
## Overview
AiEDA is an open-source AI-native Electronic Design Automation (EDA) library that revolutionizes chip design workflows by introducing a unified design-to-vector methodology. Built on the iEDA infrastructure, it transforms diverse chip design data into standardized multi-level vector representations through modular engine integration, comprehensive data extraction frameworks, and hierarchical data management. AiEDA bridges traditional EDA tools with modern AI/ML techniques by providing complete physical design flows, programmatic data extraction capabilities, and standardized Python interfaces, establishing an AI-aided design (AAD) paradigm that enables seamless integration between EDA datasets and AI frameworks.
Data Transformation for AI-aided design (AAD)
## Software Architecture
AiEDA follows a modular architecture built on top of the iEDA infrastructure:
```
AiEDA Architecture
├── AI Layer (aieda.ai)
│ ├── Net Wirelength Prediction (TabNet)
│ ├── Design Space Exploration (DSE)
│ └── ML Model Training & Inference
├── Analysis Layer (aieda.analysis)
│ ├── Design-level Analysis
│ ├── Net Analysis
│ ├── Path Analysis
│ └── Patch Analysis
├── EDA Integration Layer (aieda.eda)
│ ├── iEDA Tool Wrappers
│ ├── Flow Management
│ └── Data Extraction
├── Data Management Layer (aieda.data)
│ ├── Database Structures
│ ├── Vector Generation
│ └── Feature Engineering
├── Workspace Management (aieda.workspace)
├── Utilities (aieda.utility)
└── GUI Interface (aieda.gui)
```
## Core Modules
### 1. AI Module (`aieda.ai`)
- **Net Wirelength Prediction**: TabNet-based models for predicting wire lengths
- **Design Space Exploration (DSE)**: Automated parameter optimization
- **Model Training**: Support for various ML frameworks and algorithms
### 2. Analysis Module (`aieda.analysis`)
- **Design Analysis**: Cell type, core usage, pin distribution analysis
- **Net Analysis**: Wire distribution and metrics correlation
- **Path Analysis**: Delay and stage analysis
- **Patch Analysis**: Map analysis, wire density, and feature correlation
### 3. EDA Integration (`aieda.eda`)
- **iEDA Tool Integration**: Wrappers for 11+ EDA tools
- Placement (iPL)
- Routing (iRT)
- Clock Tree Synthesis (iCTS)
- Static Timing Analysis (iSTA)
- Floorplanning (iFP)
- Power Analysis (iPA)
- Design Rule Checking (iDRC)
- And more...
### 4. Data Management (`aieda.data`)
- **Database Structures**: Comprehensive data models for EDA features
- **Vector Generation**: Automated feature extraction and vectorization
- **Parameters**: EDA tool parameter management
### 5. Flows (`aieda.flows`)
- **iEDA Flow Management**: Automated execution of EDA tool chains
- **Data Generation**: Automated dataset creation for ML training
- **Flow Configuration**: Flexible flow definition and execution
### 6. Workspace (`aieda.workspace`)
- **Project Management**: Workspace creation and management
- **File Organization**: Structured project directory management
### 7. Utilities (`aieda.utility`)
- **Logging**: Comprehensive logging system
- **JSON Parsing**: Configuration file management
- **Permission Management**: File and folder permission handling
### 8. GUI (`aieda.gui`)
- **Layout Visualization**: Graphical interface for design visualization
## Data Flow
The AiEDA data flow follows this pattern:
1. **Design Input** → Workspace creation with design files
2. **EDA Processing** → iEDA tools process the design through various stages
3. **Feature Extraction** → Data extraction and vectorization from EDA results
4. **AI Analysis** → ML models analyze extracted features
5. **Optimization** → AI-guided parameter optimization and design improvements
6. **Validation** → Results validation and iteration
Data Flow
```
Design Files → Workspace → iEDA Tools → Feature Extraction → AI Models → Analyse → Results
↑ ↓
└──── Feedback Loop (Optimization) ─────┘
```
## Build Methods
### Method 1: Local Installation (Python dependencies and aieda library)
1. **Clone the repository with submodules:**
```bash
git clone
cd AiEDA
git submodule update --init --recursive
```
2. **Install Python Dependencies and AiEDA library:**
We support multiple Python package managers (conda, uv, etc.). We recommend UV for its efficiency.
```bash
# Use the provided build script (recommended)
# The script builds the AiEDA library by default
./build.sh
# You can also skip the AiEDA library build using --skip-build
# (recommended for development)
./build.sh --skip-build
# Or install manually
# Install UV
pip install uv
# Create and activate virtual environment
uv venv
source .venv/bin/activate
# Install aieda using one of the following options:
# Option 1: Development mode (recommended for development)
uv pip install -e .
# Option 2: Regular installation
uv build
uv pip install dist/aieda-0.1.0-py3-none-any.whl
```
3. **Compile iEDA:**
```bash
mkdir build
cd build
cmake ..
make -j32 ieda_py
```
**Note:** Building ieda_py requires **sudo** privileges to download additional system libraries.
4. **Run Tests:**
```bash
uv run python test/test_sky130_gcd.py
```
### Method 2: Docker Build (Complete environment with all dependencies)
Docker provides a containerized environment with all dependencies pre-configured, including Python/C++ dependencies, AiEDA library, and source code.
#### Prerequisites
- Docker installed on your system
- At least 20GB of available disk space (this will be optimized in future versions)
#### Build Steps
1. **Clone the repository with submodules:**
```bash
git clone
cd AiEDA
git submodule update --init --recursive
```
2. **Build the Docker image:**
```bash
docker build -t aieda:latest .
```
3. **Run the container:**
```bash
# Test the installation
docker run --rm aieda:latest python3 test/test_sky130_gcd.py
# Run in detached mode for interactive use
docker run -dit --name myaieda aieda:latest bash
# Enter the running container
docker exec -it myaieda bash
```
**Note:** For detailed Docker instructions, refer to the build script and Dockerfile in the repository.
## Getting Started
### Basic Usage Example
```python
import aieda
from aieda.workspace import workspace_create
from aieda.flows import RunIEDA, DataGeneration
from aieda.analysis import CellTypeAnalyzer
from aieda.ai import TabNetTrainer
# Create workspace
workspace = workspace_create(directory="./my_design", design="gcd")
# Run EDA flow
run_ieda = RunIEDA(workspace)
run_ieda.run_flows()
# Generate training data
data_gen = DataGeneration(workspace)
data_gen.generate_vectors()
# Perform analysis
analyzer = CellTypeAnalyzer()
analyzer.analyze()
# Train AI model
trainer = TabNetTrainer()
trainer.train()
```
### Running Tests
```bash
# Test iEDA backend
python test/test_ieda_flows.py
# Test design-level analysis
python test/test_analysis_design.py
# Test AI model training
python test/test_ai_task_placement.py
```
## Key Features
- **AI-Native Design**: Built from ground up with AI/ML integration
- **Comprehensive EDA Integration**: Support for 11+ EDA tools via iEDA
- **Automated Workflows**: End-to-end automation from design to optimization
- **Extensible Architecture**: Modular design for easy extension and customization
- **Production Ready**: Proven with 4+ successful tape-outs
- **Open Source**: Fully open-source with active community support
## Research and Publications
- **AiEDA2.0: An Open-source AI-Aided Design (AAD) Library for Design-to-Vector**, ISEDA, 2025
- **iEDA: An Open-source infrastructure of EDA**, ASPDAC, 2024
- **iPD: An Open-source intelligent Physical Design Tool Chain**, ASPDAC, 2024
## Contributing
We welcome contributions! Please see our contributing guidelines and feel free to submit issues and pull requests.
## License
This project is open-source. Please refer to the LICENSE file for details.
## Support
For questions and support, please visit our documentation (https://ieda.oscc.cc/en/aieda/library/) and the deepwiki (https://deepwiki.com/OSCC-Project/AiEDA) or contact us (https://ieda.oscc.cc/en/publicity/connection.html)
