Machine-learning offers opportunities to enable self-optimizing design tools. Very much like self-driving cars that observe real-world interactions to improve their responses in different (local) driving conditions, AI-enhanced tools are able to learn and improve in (local) design environments after deployment.
These new, ML-driven capabilities can be embedded in different design engines, giving EDA developers a new arsenal of solutions for today's demanding semiconductor design environment. Given the abundance of data and a rich set of heuristics, new classes of ML models can be created using ensemble methods (e.g., linear regression, support vector machines, neural networks) to exploit opportunities throughout the design cycle.
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