Chip Design

Use AI to accelerate and optimize semiconductor chip design and verification workflows

Business impact

• Time to market — AI shortens simulation and verification, enabling faster product launches
• Engineering productivity — Automating repetitive tasks frees engineers to focus on innovation
• Design cycle time — AI-driven workflows reduce iteration times and speed up design phases
• Verification speed — AI accelerates verification coverage and bug detection processes

Data requirements

• Proprietary chip design databases (Structured) — Used to train AI models on historical design parameters and outcomes
• Simulation and verification logs (Text) — Provide feedback data for AI to optimize design and testing processes
• EDA tool outputs (Numeric) — Supply detailed design metrics and performance data for AI analysis

AI methods and techniques

• Reinforcement learning — Optimizes chip layout and floorplanning through trial-and-error learning
• Generative AI — Generates RTL code, testbenches, and documentation to assist engineers
• Agentic AI — Automates repetitive design tasks and workflow orchestration autonomously

AI models and model families

GPT-4o, Claude, Llama, Custom reinforcement learning agents

Industries

Real-world evidence

1 documented case study on record.

Companies using this: NVIDIA.