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Machine Learning — Everywhere: Enabling Self-Optimizing Design Platforms for Better End-to-End Results

New classes of ML models can be created to exploit opportunities throughout the design cycle.

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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.

Click here to download the white paper.



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