Enterprise AI Analysis
Invited: AI-assisted Routing
Leveraging AI for Enhanced Efficiency and Quality in Routing
This paper presents a systematic methodology for AI-assisted routing in physical synthesis. By decoupling routing components, AI can be strategically applied to improve efficiency and quality while maintaining interpretability. Two applications, shallow light tree construction and multi-net routing, demonstrate the practical implementation and effectiveness of this approach.
Key Benefits for Your Enterprise
Integrating AI into routing processes can yield significant improvements in design automation, reducing time-to-market and enhancing product quality.
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Shallow Light Tree Improvement
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Multi-Net Routing Violations Reduction
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Routing Length Improvement
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
NP-complete
Rectilinear Steiner Minimum Tree (RSMT)
Constructing a rectilinear Steiner minimum tree is a computationally complex problem, highlighting the need for efficient AI-assisted solutions.
Enterprise Process Flow
Input (I) to AI Agent (A)
→
AI Agent (A) generates Prior Knowledge (P)
→
Prior Knowledge (P) modulates Router (R)
→
Router (R) produces Optimized Output (O)
| Idea | Approach | Key Features |
|---|---|---|
| AI Predicts Routing Solution | Direct adoption of AI output. |
|
| AI Assists Routing Process | AI prediction as potential guidance. |
|
AI-assisted Shallow Light Tree Construction
This application leverages AI to improve the quality of shallow light trees, balancing shallowness and lightness for better timing and wirelength. A GNN-based model predicts a 'Critical Set' of points for reconstruction, leading to superior tree structures compared to existing heuristics.
- Key Results:
- Outperforms SALT and TreeNet in shallowness and lightness across various wirelength constraints.
- Achieves up to 11.14% improvement over SALT for overall shallowness.
- Utilizes reinforcement learning to discover optimal prior knowledge for tree construction.
AI-assisted Multi-Net Routing
AI is employed to guide the rip-up and reroute (RRR) process in multi-net routing, accelerating solutions and reducing violations. A transformer-based generative auto-regressive model predicts 3D routing solutions, synthesizing guide maps that modulate the costing scheme of existing maze routing algorithms.
- Key Results:
- Significantly reduces the number of violations faster than default RRR algorithms.
- Improves total routing length by avoiding unnecessary detours.
- Utilizes multi-scale predictions and teacher-forcing for stable and accelerated training.
Non-Differentiable
Router (R) Assumption
The AI-assisted framework assumes the Router (R) is non-differentiable, making direct gradient back-propagation difficult and necessitating supervised or reinforcement learning approaches for AI agent training.
Calculate Your Potential ROI
Estimate the financial and operational benefits of integrating AI-assisted routing into your design workflow.
Annual Savings
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Hours Reclaimed Annually
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Your AI Implementation Roadmap
A structured approach to integrating AI-assisted routing, ensuring a smooth transition and maximum impact.
Phase 1: Assessment & Strategy
Conduct a comprehensive analysis of current routing workflows, identify key bottlenecks, and define specific objectives for AI integration. Develop a tailored strategy aligning with enterprise goals.
Phase 2: Pilot Program & Customization
Implement a pilot AI-assisted routing solution on a smaller scale. Customize the AI models (e.g., GNN, Transformer) to your specific design rules and data, ensuring optimal performance.
Phase 3: Integration & Training
Integrate the AI-assisted tools into your existing EDA environment. Provide comprehensive training for your engineering teams on leveraging the new AI capabilities effectively.
Phase 4: Scaling & Optimization
Expand the AI-assisted routing solution across your entire design pipeline. Continuously monitor performance, gather feedback, and iterate on AI models for ongoing optimization and improvement.
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