AI Meets RTL: Intelligent Cell Selection in VLSI Design

In the world of modern VLSI design, optimising area, power, and timing is a constant challenge. One of the key opportunities for optimisation lies in standard cell selection—deciding which version of a logic gate to use in each part of the circuit. Traditionally, this is handled by Electronic Design Automation (EDA) tools using static heuristic rules and timing analysis. But what if AI could learn from past designs and make these decisions faster and better?

Welcome to the future of RTL synthesis: AI-guided cell selection

The Problem: Finding the Right Brick for the Job

Imagine building a house using bricks of different sizes. You want to use the smallest (and most cost-effective) brick possible that still supports the structure safely. VLSI cell selection is very similar.

In your standard cell library, you might have different types of inverters:

• INV_X1 – Smallest area, slowest performance
• INV_X2 – Moderate area and speed
• INV_X4 – Largest area, fastest drive strength

Using a larger, faster cell than required increases area and power unnecessarily. The goal is to use the smallest viable cell that still meets timing constraints.

Where AI Steps In

An AI model can learn from historical design data to recommend the best cell size given:

• Input slew
• Output load
• Timing slack
• Required drive strength
• Cell delay and area

Training the AI Model

To train the model, extract data from the standard cell library (.lib file) and convert it to a structured format such as CSV using a Python script.

 Possible Models can be 

• MLP (Multi-Layer Perceptron): Good for predicting delay/area (regression)
• Random Forest or Decision Trees: Great for cell classification
• XGBoost: Powerful for tabular data with mixed interactions

Integrating with the Synthesis Flow

Once trained, the AI model can be integrated into the design flow to assist synthesis tools like Synopsys Design Compiler, Cadence Genus, or Vivado.

Integration Steps:

• Run synthesis normally to generate a netlist and timing report.
• Extract critical path and gate information.
• Feed relevant gate data (slew, load, slack) into the AI model.
• Replace oversized or suboptimal cells with AI-suggested alternatives.

Conclusion

AI is not here to replace synthesis tools—it’s here to enhance them. With machine learning models trained on historical design data, engineers can now make data-driven decisions during cell selection, thereby accelerating time-to-market and optimising in ways that traditional EDA tools alone cannot. As designs grow more complex, AI meets RTL is not just a trend—it’s becoming a necessity.