Chip Design

Use AI to automate and optimize semiconductor chip design for faster, higher-quality outcomes

Business impact

• Time to Market — Shortens development cycles enabling faster product launches and competitive advantage
• Engineering Productivity — Automates repetitive tasks freeing engineers to focus on innovation and complex problems
• Design Efficiency — Optimizes power, performance, and area trade-offs for superior chip designs
• Cost Reduction — Lowers design and verification costs through AI-driven automation and fewer iterations
• Product Quality — Improves verification coverage and reduces errors for more reliable silicon outcomes

Data requirements

• Proprietary chip design databases (Structured) — Used for training AI models on design parameters and constraints
• EDA tool logs and telemetry (Numeric) — Provide feedback on design iterations and verification results
• Design specification documents (Text) — Textual data used by generative AI for code generation and documentation
• Simulation outputs (Numeric) — Used to validate AI-generated designs and optimize performance metrics

AI methods and techniques

• Reinforcement Learning — Explores large design spaces to optimize chip parameters iteratively
• Generative AI — Generates design code, testbenches, and documentation to assist engineers
• Agentic AI — Automates multi-step design and verification tasks with minimal human input

AI models and model families

GPT-4o, Claude, Llama, AlphaEvolve

Industries

Real-world evidence

12 documented case studies on record.

Companies using this: Absolics, Cadence, Centre Development Advanced Computing C DAC, Conductor, Google, Intel, NVIDIA, Qualcomm, SEALSQ, Si Five, Tesla, Veri Silicon.