An Open-Source AI-Aided Design Library for Design-to-Vector

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 → EDA 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:

   git clone <repository-url>
   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.

   # 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:

   mkdir build
   cd build
   cmake ..
   make -j32 ieda_py

   Note: Building ieda_py requires sudo privileges to download additional system libraries.

4. Run Tests:

   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:

   git clone <repository-url>
   cd AiEDA
   git submodule update --init --recursive

2. Build the Docker image:

   docker build -t aieda:latest .

3. Run the container:

   # 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

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

# 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)